#
Auto Classes

In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you are supplying to the `from_pretrained()` method. AutoClasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.

Instantiating one of AutoConfig, AutoModel, and AutoTokenizer will directly create a class of the relevant architecture. For instance

    model = AutoModel.from_pretrained("google-bert/bert-base-cased")

will create a model that is an instance of BertModel.

There is one class of `AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax).

##
Extending the Auto Classes

Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a custom class of model `NewModel`, make sure you have a `NewModelConfig` then you can add those to the auto classes like this:

    from transformers import AutoConfig, AutoModel

    AutoConfig.register("new-model", NewModelConfig)
    AutoModel.register(NewModelConfig, NewModel)

You will then be able to use the auto classes like you would usually do!

If your `NewModelConfig` is a subclass of PretrainedConfig, make sure its `model_type` attribute is set to the same key you use when registering the config (here `"new-model"`).

Likewise, if your `NewModel` is a subclass of PreTrainedModel, make sure its `config_class` attribute is set to the same class you use when registering the model (here `NewModelConfig`).

##
AutoConfig

###
class transformers.AutoConfig

< source >

( )

This is a generic configuration class that will be instantiated as one of the configuration classes of the library when created with the from_pretrained() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_pretrained

< source >

( pretrained_model_name_or_path **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co.
  - A path to a directory containing a configuration file saved using the save_pretrained() method, or the save_pretrained() method, e.g., `./my_model_directory/`.
  - A path or url to a saved configuration JSON file, e.g., `./my_model_directory/configuration.json`.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- return_unused_kwargs (`bool`, optional, defaults to `False`) — If `False`, then this function returns just the final configuration object.

  If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where unused_kwargs is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the part of `kwargs` which has not been used to update `config` and is otherwise ignored.

- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- kwargs(additional keyword arguments, optional) — The values in kwargs of any keys which are configuration attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are not configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.

Instantiate one of the configuration classes of the library from a pretrained model configuration.

The configuration class to instantiate is selected based on the `model_type` property of the config object that is loaded, or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertConfig (ALBERT model)
- align — AlignConfig (ALIGN model)
- altclip — AltCLIPConfig (AltCLIP model)
- audio-spectrogram-transformer — ASTConfig (Audio Spectrogram Transformer model)
- autoformer — AutoformerConfig (Autoformer model)
- bark — BarkConfig (Bark model)
- bart — BartConfig (BART model)
- beit — BeitConfig (BEiT model)
- bert — BertConfig (BERT model)
- bert-generation — BertGenerationConfig (Bert Generation model)
- big_bird — BigBirdConfig (BigBird model)
- bigbird_pegasus — BigBirdPegasusConfig (BigBird-Pegasus model)
- biogpt — BioGptConfig (BioGpt model)
- bit — BitConfig (BiT model)
- blenderbot — BlenderbotConfig (Blenderbot model)
- blenderbot-small — BlenderbotSmallConfig (BlenderbotSmall model)
- blip — BlipConfig (BLIP model)
- blip-2 — Blip2Config (BLIP-2 model)
- bloom — BloomConfig (BLOOM model)
- bridgetower — BridgeTowerConfig (BridgeTower model)
- bros — BrosConfig (BROS model)
- camembert — CamembertConfig (CamemBERT model)
- canine — CanineConfig (CANINE model)
- chinese_clip — ChineseCLIPConfig (Chinese-CLIP model)
- chinese_clip_vision_model — ChineseCLIPVisionConfig (ChineseCLIPVisionModel model)
- clap — ClapConfig (CLAP model)
- clip — CLIPConfig (CLIP model)
- clip_vision_model — CLIPVisionConfig (CLIPVisionModel model)
- clipseg — CLIPSegConfig (CLIPSeg model)
- clvp — ClvpConfig (CLVP model)
- code_llama — LlamaConfig (CodeLlama model)
- codegen — CodeGenConfig (CodeGen model)
- conditional_detr — ConditionalDetrConfig (Conditional DETR model)
- convbert — ConvBertConfig (ConvBERT model)
- convnext — ConvNextConfig (ConvNeXT model)
- convnextv2 — ConvNextV2Config (ConvNeXTV2 model)
- cpmant — CpmAntConfig (CPM-Ant model)
- ctrl — CTRLConfig (CTRL model)
- cvt — CvtConfig (CvT model)
- data2vec-audio — Data2VecAudioConfig (Data2VecAudio model)
- data2vec-text — Data2VecTextConfig (Data2VecText model)
- data2vec-vision — Data2VecVisionConfig (Data2VecVision model)
- deberta — DebertaConfig (DeBERTa model)
- deberta-v2 — DebertaV2Config (DeBERTa-v2 model)
- decision_transformer — DecisionTransformerConfig (Decision Transformer model)
- deformable_detr — DeformableDetrConfig (Deformable DETR model)
- deit — DeiTConfig (DeiT model)
- depth_anything — DepthAnythingConfig (Depth Anything model)
- deta — DetaConfig (DETA model)
- detr — DetrConfig (DETR model)
- dinat — DinatConfig (DiNAT model)
- dinov2 — Dinov2Config (DINOv2 model)
- distilbert — DistilBertConfig (DistilBERT model)
- donut-swin — DonutSwinConfig (DonutSwin model)
- dpr — DPRConfig (DPR model)
- dpt — DPTConfig (DPT model)
- efficientformer — EfficientFormerConfig (EfficientFormer model)
- efficientnet — EfficientNetConfig (EfficientNet model)
- electra — ElectraConfig (ELECTRA model)
- encodec — EncodecConfig (EnCodec model)
- encoder-decoder — EncoderDecoderConfig (Encoder decoder model)
- ernie — ErnieConfig (ERNIE model)
- ernie_m — ErnieMConfig (ErnieM model)
- esm — EsmConfig (ESM model)
- falcon — FalconConfig (Falcon model)
- fastspeech2_conformer — FastSpeech2ConformerConfig (FastSpeech2Conformer model)
- flaubert — FlaubertConfig (FlauBERT model)
- flava — FlavaConfig (FLAVA model)
- fnet — FNetConfig (FNet model)
- focalnet — FocalNetConfig (FocalNet model)
- fsmt — FSMTConfig (FairSeq Machine-Translation model)
- funnel — FunnelConfig (Funnel Transformer model)
- fuyu — FuyuConfig (Fuyu model)
- gemma — GemmaConfig (Gemma model)
- git — GitConfig (GIT model)
- glpn — GLPNConfig (GLPN model)
- gpt-sw3 — GPT2Config (GPT-Sw3 model)
- gpt2 — GPT2Config (OpenAI GPT-2 model)
- gpt_bigcode — GPTBigCodeConfig (GPTBigCode model)
- gpt_neo — GPTNeoConfig (GPT Neo model)
- gpt_neox — GPTNeoXConfig (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseConfig (GPT NeoX Japanese model)
- gptj — GPTJConfig (GPT-J model)
- gptsan-japanese — GPTSanJapaneseConfig (GPTSAN-japanese model)
- graphormer — GraphormerConfig (Graphormer model)
- groupvit — GroupViTConfig (GroupViT model)
- hubert — HubertConfig (Hubert model)
- ibert — IBertConfig (I-BERT model)
- idefics — IdeficsConfig (IDEFICS model)
- imagegpt — ImageGPTConfig (ImageGPT model)
- informer — InformerConfig (Informer model)
- instructblip — InstructBlipConfig (InstructBLIP model)
- jukebox — JukeboxConfig (Jukebox model)
- kosmos-2 — Kosmos2Config (KOSMOS-2 model)
- layoutlm — LayoutLMConfig (LayoutLM model)
- layoutlmv2 — LayoutLMv2Config (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Config (LayoutLMv3 model)
- led — LEDConfig (LED model)
- levit — LevitConfig (LeViT model)
- lilt — LiltConfig (LiLT model)
- llama — LlamaConfig (LLaMA model)
- llava — LlavaConfig (LLaVa model)
- longformer — LongformerConfig (Longformer model)
- longt5 — LongT5Config (LongT5 model)
- luke — LukeConfig (LUKE model)
- lxmert — LxmertConfig (LXMERT model)
- m2m_100 — M2M100Config (M2M100 model)
- mamba — MambaConfig (Mamba model)
- marian — MarianConfig (Marian model)
- markuplm — MarkupLMConfig (MarkupLM model)
- mask2former — Mask2FormerConfig (Mask2Former model)
- maskformer — MaskFormerConfig (MaskFormer model)
- maskformer-swin — `MaskFormerSwinConfig` (MaskFormerSwin model)
- mbart — MBartConfig (mBART model)
- mctct — MCTCTConfig (M-CTC-T model)
- mega — MegaConfig (MEGA model)
- megatron-bert — MegatronBertConfig (Megatron-BERT model)
- mgp-str — MgpstrConfig (MGP-STR model)
- mistral — MistralConfig (Mistral model)
- mixtral — MixtralConfig (Mixtral model)
- mobilebert — MobileBertConfig (MobileBERT model)
- mobilenet_v1 — MobileNetV1Config (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2Config (MobileNetV2 model)
- mobilevit — MobileViTConfig (MobileViT model)
- mobilevitv2 — MobileViTV2Config (MobileViTV2 model)
- mpnet — MPNetConfig (MPNet model)
- mpt — MptConfig (MPT model)
- mra — MraConfig (MRA model)
- mt5 — MT5Config (MT5 model)
- musicgen — MusicgenConfig (MusicGen model)
- mvp — MvpConfig (MVP model)
- nat — NatConfig (NAT model)
- nezha — NezhaConfig (Nezha model)
- nllb-moe — NllbMoeConfig (NLLB-MOE model)
- nougat — VisionEncoderDecoderConfig (Nougat model)
- nystromformer — NystromformerConfig (Nyströmformer model)
- oneformer — OneFormerConfig (OneFormer model)
- open-llama — OpenLlamaConfig (OpenLlama model)
- openai-gpt — OpenAIGPTConfig (OpenAI GPT model)
- opt — OPTConfig (OPT model)
- owlv2 — Owlv2Config (OWLv2 model)
- owlvit — OwlViTConfig (OWL-ViT model)
- patchtsmixer — PatchTSMixerConfig (PatchTSMixer model)
- patchtst — PatchTSTConfig (PatchTST model)
- pegasus — PegasusConfig (Pegasus model)
- pegasus_x — PegasusXConfig (PEGASUS-X model)
- perceiver — PerceiverConfig (Perceiver model)
- persimmon — PersimmonConfig (Persimmon model)
- phi — PhiConfig (Phi model)
- pix2struct — Pix2StructConfig (Pix2Struct model)
- plbart — PLBartConfig (PLBart model)
- poolformer — PoolFormerConfig (PoolFormer model)
- pop2piano — Pop2PianoConfig (Pop2Piano model)
- prophetnet — ProphetNetConfig (ProphetNet model)
- pvt — PvtConfig (PVT model)
- qdqbert — QDQBertConfig (QDQBert model)
- qwen2 — Qwen2Config (Qwen2 model)
- rag — RagConfig (RAG model)
- realm — RealmConfig (REALM model)
- reformer — ReformerConfig (Reformer model)
- regnet — RegNetConfig (RegNet model)
- rembert — RemBertConfig (RemBERT model)
- resnet — ResNetConfig (ResNet model)
- retribert — RetriBertConfig (RetriBERT model)
- roberta — RobertaConfig (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormConfig (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertConfig (RoCBert model)
- roformer — RoFormerConfig (RoFormer model)
- rwkv — RwkvConfig (RWKV model)
- sam — SamConfig (SAM model)
- seamless_m4t — SeamlessM4TConfig (SeamlessM4T model)
- seamless_m4t_v2 — SeamlessM4Tv2Config (SeamlessM4Tv2 model)
- segformer — SegformerConfig (SegFormer model)
- seggpt — SegGptConfig (SegGPT model)
- sew — SEWConfig (SEW model)
- sew-d — SEWDConfig (SEW-D model)
- siglip — SiglipConfig (SigLIP model)
- siglip_vision_model — SiglipVisionConfig (SiglipVisionModel model)
- speech-encoder-decoder — SpeechEncoderDecoderConfig (Speech Encoder decoder model)
- speech_to_text — Speech2TextConfig (Speech2Text model)
- speech_to_text_2 — Speech2Text2Config (Speech2Text2 model)
- speecht5 — SpeechT5Config (SpeechT5 model)
- splinter — SplinterConfig (Splinter model)
- squeezebert — SqueezeBertConfig (SqueezeBERT model)
- stablelm — StableLmConfig (StableLm model)
- starcoder2 — Starcoder2Config (Starcoder2 model)
- swiftformer — SwiftFormerConfig (SwiftFormer model)
- swin — SwinConfig (Swin Transformer model)
- swin2sr — Swin2SRConfig (Swin2SR model)
- swinv2 — Swinv2Config (Swin Transformer V2 model)
- switch_transformers — SwitchTransformersConfig (SwitchTransformers model)
- t5 — T5Config (T5 model)
- table-transformer — TableTransformerConfig (Table Transformer model)
- tapas — TapasConfig (TAPAS model)
- time_series_transformer — TimeSeriesTransformerConfig (Time Series Transformer model)
- timesformer — TimesformerConfig (TimeSformer model)
- timm_backbone — TimmBackboneConfig (TimmBackbone model)
- trajectory_transformer — TrajectoryTransformerConfig (Trajectory Transformer model)
- transfo-xl — TransfoXLConfig (Transformer-XL model)
- trocr — TrOCRConfig (TrOCR model)
- tvlt — TvltConfig (TVLT model)
- tvp — TvpConfig (TVP model)
- udop — UdopConfig (UDOP model)
- umt5 — UMT5Config (UMT5 model)
- unispeech — UniSpeechConfig (UniSpeech model)
- unispeech-sat — UniSpeechSatConfig (UniSpeechSat model)
- univnet — UnivNetConfig (UnivNet model)
- upernet — UperNetConfig (UPerNet model)
- van — VanConfig (VAN model)
- videomae — VideoMAEConfig (VideoMAE model)
- vilt — ViltConfig (ViLT model)
- vipllava — VipLlavaConfig (VipLlava model)
- vision-encoder-decoder — VisionEncoderDecoderConfig (Vision Encoder decoder model)
- vision-text-dual-encoder — VisionTextDualEncoderConfig (VisionTextDualEncoder model)
- visual_bert — VisualBertConfig (VisualBERT model)
- vit — ViTConfig (ViT model)
- vit_hybrid — ViTHybridConfig (ViT Hybrid model)
- vit_mae — ViTMAEConfig (ViTMAE model)
- vit_msn — ViTMSNConfig (ViTMSN model)
- vitdet — VitDetConfig (VitDet model)
- vitmatte — VitMatteConfig (ViTMatte model)
- vits — VitsConfig (VITS model)
- vivit — VivitConfig (ViViT model)
- wav2vec2 — Wav2Vec2Config (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2BertConfig (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2ConformerConfig (Wav2Vec2-Conformer model)
- wavlm — WavLMConfig (WavLM model)
- whisper — WhisperConfig (Whisper model)
- xclip — XCLIPConfig (X-CLIP model)
- xglm — XGLMConfig (XGLM model)
- xlm — XLMConfig (XLM model)
- xlm-prophetnet — XLMProphetNetConfig (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaConfig (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLConfig (XLM-RoBERTa-XL model)
- xlnet — XLNetConfig (XLNet model)
- xmod — XmodConfig (X-MOD model)
- yolos — YolosConfig (YOLOS model)
- yoso — YosoConfig (YOSO model)

Examples:

    >>> from transformers import AutoConfig

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased")

    >>> # Download configuration from huggingface.co (user-uploaded) and cache.
    >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")

    >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
    >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")

    >>> # Load a specific configuration file.
    >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")

    >>> # Change some config attributes when loading a pretrained config.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-uncased", output_attentions=True, foo=False)
    >>> config.output_attentions
    True

    >>> config, unused_kwargs = AutoConfig.from_pretrained(
    ...     "google-bert/bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
    ... )
    >>> config.output_attentions
    True

    >>> unused_kwargs
    {'foo': False}

####
register

< source >

( model_type config exist_ok = False )

Parameters

- model_type (`str`) — The model type like “bert” or “gpt”.
- config (PretrainedConfig) — The config to register.

Register a new configuration for this class.

##
AutoTokenizer

###
class transformers.AutoTokenizer

< source >

( )

This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the AutoTokenizer.from_pretrained() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_pretrained

< source >

( pretrained_model_name_or_path *inputs **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a predefined tokenizer hosted inside a model repo on huggingface.co.
  - A path to a directory containing vocabulary files required by the tokenizer, for instance saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - A path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (like Bert or XLNet), e.g.: `./my_model_directory/vocab.txt`. (Not applicable to all derived classes)
- inputs (additional positional arguments, optional) — Will be passed along to the Tokenizer `__init__()` method.
- config (PretrainedConfig, optional) — The configuration object used to determine the tokenizer class to instantiate.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- subfolder (`str`, optional) — In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for facebook/rag-token-base), specify it here.
- use_fast (`bool`, optional, defaults to `True`) — Use a fast Rust-based tokenizer if it is supported for a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer is returned instead.
- tokenizer_type (`str`, optional) — Tokenizer type to be loaded.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- kwargs (additional keyword arguments, optional) — Will be passed to the Tokenizer `__init__()` method. Can be used to set special tokens like `bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`. See parameters in the `__init__()` for more details.

Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary.

The tokenizer class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertTokenizer or AlbertTokenizerFast (ALBERT model)
- align — BertTokenizer or BertTokenizerFast (ALIGN model)
- bark — BertTokenizer or BertTokenizerFast (Bark model)
- bart — BartTokenizer or BartTokenizerFast (BART model)
- barthez — BarthezTokenizer or BarthezTokenizerFast (BARThez model)
- bartpho — BartphoTokenizer (BARTpho model)
- bert — BertTokenizer or BertTokenizerFast (BERT model)
- bert-generation — BertGenerationTokenizer (Bert Generation model)
- bert-japanese — BertJapaneseTokenizer (BertJapanese model)
- bertweet — BertweetTokenizer (BERTweet model)
- big_bird — BigBirdTokenizer or BigBirdTokenizerFast (BigBird model)
- bigbird_pegasus — PegasusTokenizer or PegasusTokenizerFast (BigBird-Pegasus model)
- biogpt — BioGptTokenizer (BioGpt model)
- blenderbot — BlenderbotTokenizer or BlenderbotTokenizerFast (Blenderbot model)
- blenderbot-small — BlenderbotSmallTokenizer (BlenderbotSmall model)
- blip — BertTokenizer or BertTokenizerFast (BLIP model)
- blip-2 — GPT2Tokenizer or GPT2TokenizerFast (BLIP-2 model)
- bloom — BloomTokenizerFast (BLOOM model)
- bridgetower — RobertaTokenizer or RobertaTokenizerFast (BridgeTower model)
- bros — BertTokenizer or BertTokenizerFast (BROS model)
- byt5 — ByT5Tokenizer (ByT5 model)
- camembert — CamembertTokenizer or CamembertTokenizerFast (CamemBERT model)
- canine — CanineTokenizer (CANINE model)
- chinese_clip — BertTokenizer or BertTokenizerFast (Chinese-CLIP model)
- clap — RobertaTokenizer or RobertaTokenizerFast (CLAP model)
- clip — CLIPTokenizer or CLIPTokenizerFast (CLIP model)
- clipseg — CLIPTokenizer or CLIPTokenizerFast (CLIPSeg model)
- clvp — ClvpTokenizer (CLVP model)
- code_llama — CodeLlamaTokenizer or CodeLlamaTokenizerFast (CodeLlama model)
- codegen — CodeGenTokenizer or CodeGenTokenizerFast (CodeGen model)
- convbert — ConvBertTokenizer or ConvBertTokenizerFast (ConvBERT model)
- cpm — CpmTokenizer or CpmTokenizerFast (CPM model)
- cpmant — CpmAntTokenizer (CPM-Ant model)
- ctrl — CTRLTokenizer (CTRL model)
- data2vec-audio — Wav2Vec2CTCTokenizer (Data2VecAudio model)
- data2vec-text — RobertaTokenizer or RobertaTokenizerFast (Data2VecText model)
- deberta — DebertaTokenizer or DebertaTokenizerFast (DeBERTa model)
- deberta-v2 — DebertaV2Tokenizer or DebertaV2TokenizerFast (DeBERTa-v2 model)
- distilbert — DistilBertTokenizer or DistilBertTokenizerFast (DistilBERT model)
- dpr — DPRQuestionEncoderTokenizer or DPRQuestionEncoderTokenizerFast (DPR model)
- electra — ElectraTokenizer or ElectraTokenizerFast (ELECTRA model)
- ernie — BertTokenizer or BertTokenizerFast (ERNIE model)
- ernie_m — ErnieMTokenizer (ErnieM model)
- esm — EsmTokenizer (ESM model)
- falcon — PreTrainedTokenizerFast (Falcon model)
- fastspeech2_conformer — (FastSpeech2Conformer model)
- flaubert — FlaubertTokenizer (FlauBERT model)
- fnet — FNetTokenizer or FNetTokenizerFast (FNet model)
- fsmt — FSMTTokenizer (FairSeq Machine-Translation model)
- funnel — FunnelTokenizer or FunnelTokenizerFast (Funnel Transformer model)
- gemma — GemmaTokenizer or GemmaTokenizerFast (Gemma model)
- git — BertTokenizer or BertTokenizerFast (GIT model)
- gpt-sw3 — GPTSw3Tokenizer (GPT-Sw3 model)
- gpt2 — GPT2Tokenizer or GPT2TokenizerFast (OpenAI GPT-2 model)
- gpt_bigcode — GPT2Tokenizer or GPT2TokenizerFast (GPTBigCode model)
- gpt_neo — GPT2Tokenizer or GPT2TokenizerFast (GPT Neo model)
- gpt_neox — GPTNeoXTokenizerFast (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseTokenizer (GPT NeoX Japanese model)
- gptj — GPT2Tokenizer or GPT2TokenizerFast (GPT-J model)
- gptsan-japanese — GPTSanJapaneseTokenizer (GPTSAN-japanese model)
- groupvit — CLIPTokenizer or CLIPTokenizerFast (GroupViT model)
- herbert — HerbertTokenizer or HerbertTokenizerFast (HerBERT model)
- hubert — Wav2Vec2CTCTokenizer (Hubert model)
- ibert — RobertaTokenizer or RobertaTokenizerFast (I-BERT model)
- idefics — LlamaTokenizerFast (IDEFICS model)
- instructblip — GPT2Tokenizer or GPT2TokenizerFast (InstructBLIP model)
- jukebox — JukeboxTokenizer (Jukebox model)
- kosmos-2 — XLMRobertaTokenizer or XLMRobertaTokenizerFast (KOSMOS-2 model)
- layoutlm — LayoutLMTokenizer or LayoutLMTokenizerFast (LayoutLM model)
- layoutlmv2 — LayoutLMv2Tokenizer or LayoutLMv2TokenizerFast (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Tokenizer or LayoutLMv3TokenizerFast (LayoutLMv3 model)
- layoutxlm — LayoutXLMTokenizer or LayoutXLMTokenizerFast (LayoutXLM model)
- led — LEDTokenizer or LEDTokenizerFast (LED model)
- lilt — LayoutLMv3Tokenizer or LayoutLMv3TokenizerFast (LiLT model)
- llama — LlamaTokenizer or LlamaTokenizerFast (LLaMA model)
- llava — LlamaTokenizer or LlamaTokenizerFast (LLaVa model)
- longformer — LongformerTokenizer or LongformerTokenizerFast (Longformer model)
- longt5 — T5Tokenizer or T5TokenizerFast (LongT5 model)
- luke — LukeTokenizer (LUKE model)
- lxmert — LxmertTokenizer or LxmertTokenizerFast (LXMERT model)
- m2m_100 — M2M100Tokenizer (M2M100 model)
- mamba — GPTNeoXTokenizerFast (Mamba model)
- marian — MarianTokenizer (Marian model)
- mbart — MBartTokenizer or MBartTokenizerFast (mBART model)
- mbart50 — MBart50Tokenizer or MBart50TokenizerFast (mBART-50 model)
- mega — RobertaTokenizer or RobertaTokenizerFast (MEGA model)
- megatron-bert — BertTokenizer or BertTokenizerFast (Megatron-BERT model)
- mgp-str — MgpstrTokenizer (MGP-STR model)
- mistral — LlamaTokenizer or LlamaTokenizerFast (Mistral model)
- mixtral — LlamaTokenizer or LlamaTokenizerFast (Mixtral model)
- mluke — MLukeTokenizer (mLUKE model)
- mobilebert — MobileBertTokenizer or MobileBertTokenizerFast (MobileBERT model)
- mpnet — MPNetTokenizer or MPNetTokenizerFast (MPNet model)
- mpt — GPTNeoXTokenizerFast (MPT model)
- mra — RobertaTokenizer or RobertaTokenizerFast (MRA model)
- mt5 — MT5Tokenizer or MT5TokenizerFast (MT5 model)
- musicgen — T5Tokenizer or T5TokenizerFast (MusicGen model)
- mvp — MvpTokenizer or MvpTokenizerFast (MVP model)
- nezha — BertTokenizer or BertTokenizerFast (Nezha model)
- nllb — NllbTokenizer or NllbTokenizerFast (NLLB model)
- nllb-moe — NllbTokenizer or NllbTokenizerFast (NLLB-MOE model)
- nystromformer — AlbertTokenizer or AlbertTokenizerFast (Nyströmformer model)
- oneformer — CLIPTokenizer or CLIPTokenizerFast (OneFormer model)
- openai-gpt — OpenAIGPTTokenizer or OpenAIGPTTokenizerFast (OpenAI GPT model)
- opt — GPT2Tokenizer or GPT2TokenizerFast (OPT model)
- owlv2 — CLIPTokenizer or CLIPTokenizerFast (OWLv2 model)
- owlvit — CLIPTokenizer or CLIPTokenizerFast (OWL-ViT model)
- pegasus — PegasusTokenizer or PegasusTokenizerFast (Pegasus model)
- pegasus_x — PegasusTokenizer or PegasusTokenizerFast (PEGASUS-X model)
- perceiver — PerceiverTokenizer (Perceiver model)
- persimmon — LlamaTokenizer or LlamaTokenizerFast (Persimmon model)
- phi — CodeGenTokenizer or CodeGenTokenizerFast (Phi model)
- phobert — PhobertTokenizer (PhoBERT model)
- pix2struct — T5Tokenizer or T5TokenizerFast (Pix2Struct model)
- plbart — PLBartTokenizer (PLBart model)
- prophetnet — ProphetNetTokenizer (ProphetNet model)
- qdqbert — BertTokenizer or BertTokenizerFast (QDQBert model)
- qwen2 — Qwen2Tokenizer or Qwen2TokenizerFast (Qwen2 model)
- rag — RagTokenizer (RAG model)
- realm — RealmTokenizer or RealmTokenizerFast (REALM model)
- reformer — ReformerTokenizer or ReformerTokenizerFast (Reformer model)
- rembert — RemBertTokenizer or RemBertTokenizerFast (RemBERT model)
- retribert — RetriBertTokenizer or RetriBertTokenizerFast (RetriBERT model)
- roberta — RobertaTokenizer or RobertaTokenizerFast (RoBERTa model)
- roberta-prelayernorm — RobertaTokenizer or RobertaTokenizerFast (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertTokenizer (RoCBert model)
- roformer — RoFormerTokenizer or RoFormerTokenizerFast (RoFormer model)
- rwkv — GPTNeoXTokenizerFast (RWKV model)
- seamless_m4t — SeamlessM4TTokenizer or SeamlessM4TTokenizerFast (SeamlessM4T model)
- seamless_m4t_v2 — SeamlessM4TTokenizer or SeamlessM4TTokenizerFast (SeamlessM4Tv2 model)
- siglip — SiglipTokenizer (SigLIP model)
- speech_to_text — Speech2TextTokenizer (Speech2Text model)
- speech_to_text_2 — Speech2Text2Tokenizer (Speech2Text2 model)
- speecht5 — SpeechT5Tokenizer (SpeechT5 model)
- splinter — SplinterTokenizer or SplinterTokenizerFast (Splinter model)
- squeezebert — SqueezeBertTokenizer or SqueezeBertTokenizerFast (SqueezeBERT model)
- stablelm — GPTNeoXTokenizerFast (StableLm model)
- starcoder2 — GPT2Tokenizer or GPT2TokenizerFast (Starcoder2 model)
- switch_transformers — T5Tokenizer or T5TokenizerFast (SwitchTransformers model)
- t5 — T5Tokenizer or T5TokenizerFast (T5 model)
- tapas — TapasTokenizer (TAPAS model)
- tapex — TapexTokenizer (TAPEX model)
- transfo-xl — TransfoXLTokenizer (Transformer-XL model)
- tvp — BertTokenizer or BertTokenizerFast (TVP model)
- udop — UdopTokenizer or UdopTokenizerFast (UDOP model)
- umt5 — T5Tokenizer or T5TokenizerFast (UMT5 model)
- vilt — BertTokenizer or BertTokenizerFast (ViLT model)
- vipllava — LlamaTokenizer or LlamaTokenizerFast (VipLlava model)
- visual_bert — BertTokenizer or BertTokenizerFast (VisualBERT model)
- vits — VitsTokenizer (VITS model)
- wav2vec2 — Wav2Vec2CTCTokenizer (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2CTCTokenizer (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2CTCTokenizer (Wav2Vec2-Conformer model)
- wav2vec2_phoneme — Wav2Vec2PhonemeCTCTokenizer (Wav2Vec2Phoneme model)
- whisper — WhisperTokenizer or WhisperTokenizerFast (Whisper model)
- xclip — CLIPTokenizer or CLIPTokenizerFast (X-CLIP model)
- xglm — XGLMTokenizer or XGLMTokenizerFast (XGLM model)
- xlm — XLMTokenizer (XLM model)
- xlm-prophetnet — XLMProphetNetTokenizer (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaTokenizer or XLMRobertaTokenizerFast (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaTokenizer or XLMRobertaTokenizerFast (XLM-RoBERTa-XL model)
- xlnet — XLNetTokenizer or XLNetTokenizerFast (XLNet model)
- xmod — XLMRobertaTokenizer or XLMRobertaTokenizerFast (X-MOD model)
- yoso — AlbertTokenizer or AlbertTokenizerFast (YOSO model)

Examples:

    >>> from transformers import AutoTokenizer

    >>> # Download vocabulary from huggingface.co and cache.
    >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")

    >>> # Download vocabulary from huggingface.co (user-uploaded) and cache.
    >>> tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")

    >>> # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*)
    >>> # tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/")

    >>> # Download vocabulary from huggingface.co and define model-specific arguments
    >>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base", add_prefix_space=True)

####
register

< source >

( config_class slow_tokenizer_class = None fast_tokenizer_class = None exist_ok = False )

Parameters

- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
- slow_tokenizer_class (`PretrainedTokenizer`, optional) — The slow tokenizer to register.
- fast_tokenizer_class (`PretrainedTokenizerFast`, optional) — The fast tokenizer to register.

Register a new tokenizer in this mapping.

##
AutoFeatureExtractor

###
class transformers.AutoFeatureExtractor

< source >

( )

This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the library when created with the AutoFeatureExtractor.from_pretrained() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_pretrained

< source >

( pretrained_model_name_or_path **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — This can be either:
  - a string, the model id of a pretrained feature_extractor hosted inside a model repo on huggingface.co.
  - a path to a directory containing a feature extractor file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - a path or url to a saved feature extractor JSON file, e.g., `./my_model_directory/preprocessor_config.json`.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model feature extractor should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force to (re-)download the feature extractor files and override the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- return_unused_kwargs (`bool`, optional, defaults to `False`) — If `False`, then this function returns just the final feature extractor object. If `True`, then this functions returns a `Tuple(feature_extractor, unused_kwargs)` where unused_kwargs is a dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- kwargs (`Dict[str, Any]`, optional) — The values in kwargs of any keys which are feature extractor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are not feature extractor attributes is controlled by the `return_unused_kwargs` keyword parameter.

Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.

The feature extractor class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- audio-spectrogram-transformer — ASTFeatureExtractor (Audio Spectrogram Transformer model)
- beit — BeitFeatureExtractor (BEiT model)
- chinese_clip — ChineseCLIPFeatureExtractor (Chinese-CLIP model)
- clap — ClapFeatureExtractor (CLAP model)
- clip — CLIPFeatureExtractor (CLIP model)
- clipseg — ViTFeatureExtractor (CLIPSeg model)
- clvp — ClvpFeatureExtractor (CLVP model)
- conditional_detr — ConditionalDetrFeatureExtractor (Conditional DETR model)
- convnext — ConvNextFeatureExtractor (ConvNeXT model)
- cvt — ConvNextFeatureExtractor (CvT model)
- data2vec-audio — Wav2Vec2FeatureExtractor (Data2VecAudio model)
- data2vec-vision — BeitFeatureExtractor (Data2VecVision model)
- deformable_detr — DeformableDetrFeatureExtractor (Deformable DETR model)
- deit — DeiTFeatureExtractor (DeiT model)
- detr — DetrFeatureExtractor (DETR model)
- dinat — ViTFeatureExtractor (DiNAT model)
- donut-swin — DonutFeatureExtractor (DonutSwin model)
- dpt — DPTFeatureExtractor (DPT model)
- encodec — EncodecFeatureExtractor (EnCodec model)
- flava — FlavaFeatureExtractor (FLAVA model)
- glpn — GLPNFeatureExtractor (GLPN model)
- groupvit — CLIPFeatureExtractor (GroupViT model)
- hubert — Wav2Vec2FeatureExtractor (Hubert model)
- imagegpt — ImageGPTFeatureExtractor (ImageGPT model)
- layoutlmv2 — LayoutLMv2FeatureExtractor (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3FeatureExtractor (LayoutLMv3 model)
- levit — LevitFeatureExtractor (LeViT model)
- maskformer — MaskFormerFeatureExtractor (MaskFormer model)
- mctct — MCTCTFeatureExtractor (M-CTC-T model)
- mobilenet_v1 — MobileNetV1FeatureExtractor (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2FeatureExtractor (MobileNetV2 model)
- mobilevit — MobileViTFeatureExtractor (MobileViT model)
- nat — ViTFeatureExtractor (NAT model)
- owlvit — OwlViTFeatureExtractor (OWL-ViT model)
- perceiver — PerceiverFeatureExtractor (Perceiver model)
- poolformer — PoolFormerFeatureExtractor (PoolFormer model)
- pop2piano — Pop2PianoFeatureExtractor (Pop2Piano model)
- regnet — ConvNextFeatureExtractor (RegNet model)
- resnet — ConvNextFeatureExtractor (ResNet model)
- seamless_m4t — SeamlessM4TFeatureExtractor (SeamlessM4T model)
- seamless_m4t_v2 — SeamlessM4TFeatureExtractor (SeamlessM4Tv2 model)
- segformer — SegformerFeatureExtractor (SegFormer model)
- sew — Wav2Vec2FeatureExtractor (SEW model)
- sew-d — Wav2Vec2FeatureExtractor (SEW-D model)
- speech_to_text — Speech2TextFeatureExtractor (Speech2Text model)
- speecht5 — SpeechT5FeatureExtractor (SpeechT5 model)
- swiftformer — ViTFeatureExtractor (SwiftFormer model)
- swin — ViTFeatureExtractor (Swin Transformer model)
- swinv2 — ViTFeatureExtractor (Swin Transformer V2 model)
- table-transformer — DetrFeatureExtractor (Table Transformer model)
- timesformer — VideoMAEFeatureExtractor (TimeSformer model)
- tvlt — TvltFeatureExtractor (TVLT model)
- unispeech — Wav2Vec2FeatureExtractor (UniSpeech model)
- unispeech-sat — Wav2Vec2FeatureExtractor (UniSpeechSat model)
- univnet — UnivNetFeatureExtractor (UnivNet model)
- van — ConvNextFeatureExtractor (VAN model)
- videomae — VideoMAEFeatureExtractor (VideoMAE model)
- vilt — ViltFeatureExtractor (ViLT model)
- vit — ViTFeatureExtractor (ViT model)
- vit_mae — ViTFeatureExtractor (ViTMAE model)
- vit_msn — ViTFeatureExtractor (ViTMSN model)
- wav2vec2 — Wav2Vec2FeatureExtractor (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2FeatureExtractor (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2FeatureExtractor (Wav2Vec2-Conformer model)
- wavlm — Wav2Vec2FeatureExtractor (WavLM model)
- whisper — WhisperFeatureExtractor (Whisper model)
- xclip — CLIPFeatureExtractor (X-CLIP model)
- yolos — YolosFeatureExtractor (YOLOS model)

Passing `token=True` is required when you want to use a private model.

Examples:

    >>> from transformers import AutoFeatureExtractor

    >>> # Download feature extractor from huggingface.co and cache.
    >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")

    >>> # If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained('./test/saved_model/')*)
    >>> # feature_extractor = AutoFeatureExtractor.from_pretrained("./test/saved_model/")

####
register

< source >

( config_class feature_extractor_class exist_ok = False )

Parameters

- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
- feature_extractor_class (`FeatureExtractorMixin`) — The feature extractor to register.

Register a new feature extractor for this class.

##
AutoImageProcessor

###
class transformers.AutoImageProcessor

< source >

( )

This is a generic image processor class that will be instantiated as one of the image processor classes of the library when created with the AutoImageProcessor.from_pretrained() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_pretrained

< source >

( pretrained_model_name_or_path **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — This can be either:
  - a string, the model id of a pretrained image_processor hosted inside a model repo on huggingface.co.
  - a path to a directory containing a image processor file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - a path or url to a saved image processor JSON file, e.g., `./my_model_directory/preprocessor_config.json`.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model image processor should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force to (re-)download the image processor files and override the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- return_unused_kwargs (`bool`, optional, defaults to `False`) — If `False`, then this function returns just the final image processor object. If `True`, then this functions returns a `Tuple(image_processor, unused_kwargs)` where unused_kwargs is a dictionary consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of `kwargs` which has not been used to update `image_processor` and is otherwise ignored.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- kwargs (`Dict[str, Any]`, optional) — The values in kwargs of any keys which are image processor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are not image processor attributes is controlled by the `return_unused_kwargs` keyword parameter.

Instantiate one of the image processor classes of the library from a pretrained model vocabulary.

The image processor class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- align — EfficientNetImageProcessor (ALIGN model)
- beit — BeitImageProcessor (BEiT model)
- bit — BitImageProcessor (BiT model)
- blip — BlipImageProcessor (BLIP model)
- blip-2 — BlipImageProcessor (BLIP-2 model)
- bridgetower — BridgeTowerImageProcessor (BridgeTower model)
- chinese_clip — ChineseCLIPImageProcessor (Chinese-CLIP model)
- clip — CLIPImageProcessor (CLIP model)
- clipseg — ViTImageProcessor (CLIPSeg model)
- conditional_detr — ConditionalDetrImageProcessor (Conditional DETR model)
- convnext — ConvNextImageProcessor (ConvNeXT model)
- convnextv2 — ConvNextImageProcessor (ConvNeXTV2 model)
- cvt — ConvNextImageProcessor (CvT model)
- data2vec-vision — BeitImageProcessor (Data2VecVision model)
- deformable_detr — DeformableDetrImageProcessor (Deformable DETR model)
- deit — DeiTImageProcessor (DeiT model)
- depth_anything — DPTImageProcessor (Depth Anything model)
- deta — DetaImageProcessor (DETA model)
- detr — DetrImageProcessor (DETR model)
- dinat — ViTImageProcessor (DiNAT model)
- dinov2 — BitImageProcessor (DINOv2 model)
- donut-swin — DonutImageProcessor (DonutSwin model)
- dpt — DPTImageProcessor (DPT model)
- efficientformer — EfficientFormerImageProcessor (EfficientFormer model)
- efficientnet — EfficientNetImageProcessor (EfficientNet model)
- flava — FlavaImageProcessor (FLAVA model)
- focalnet — BitImageProcessor (FocalNet model)
- fuyu — FuyuImageProcessor (Fuyu model)
- git — CLIPImageProcessor (GIT model)
- glpn — GLPNImageProcessor (GLPN model)
- groupvit — CLIPImageProcessor (GroupViT model)
- idefics — IdeficsImageProcessor (IDEFICS model)
- imagegpt — ImageGPTImageProcessor (ImageGPT model)
- instructblip — BlipImageProcessor (InstructBLIP model)
- kosmos-2 — CLIPImageProcessor (KOSMOS-2 model)
- layoutlmv2 — LayoutLMv2ImageProcessor (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ImageProcessor (LayoutLMv3 model)
- levit — LevitImageProcessor (LeViT model)
- llava — CLIPImageProcessor (LLaVa model)
- mask2former — Mask2FormerImageProcessor (Mask2Former model)
- maskformer — MaskFormerImageProcessor (MaskFormer model)
- mgp-str — ViTImageProcessor (MGP-STR model)
- mobilenet_v1 — MobileNetV1ImageProcessor (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2ImageProcessor (MobileNetV2 model)
- mobilevit — MobileViTImageProcessor (MobileViT model)
- mobilevitv2 — MobileViTImageProcessor (MobileViTV2 model)
- nat — ViTImageProcessor (NAT model)
- nougat — NougatImageProcessor (Nougat model)
- oneformer — OneFormerImageProcessor (OneFormer model)
- owlv2 — Owlv2ImageProcessor (OWLv2 model)
- owlvit — OwlViTImageProcessor (OWL-ViT model)
- perceiver — PerceiverImageProcessor (Perceiver model)
- pix2struct — Pix2StructImageProcessor (Pix2Struct model)
- poolformer — PoolFormerImageProcessor (PoolFormer model)
- pvt — PvtImageProcessor (PVT model)
- regnet — ConvNextImageProcessor (RegNet model)
- resnet — ConvNextImageProcessor (ResNet model)
- sam — SamImageProcessor (SAM model)
- segformer — SegformerImageProcessor (SegFormer model)
- seggpt — SegGptImageProcessor (SegGPT model)
- siglip — SiglipImageProcessor (SigLIP model)
- swiftformer — ViTImageProcessor (SwiftFormer model)
- swin — ViTImageProcessor (Swin Transformer model)
- swin2sr — Swin2SRImageProcessor (Swin2SR model)
- swinv2 — ViTImageProcessor (Swin Transformer V2 model)
- table-transformer — DetrImageProcessor (Table Transformer model)
- timesformer — VideoMAEImageProcessor (TimeSformer model)
- tvlt — TvltImageProcessor (TVLT model)
- tvp — TvpImageProcessor (TVP model)
- udop — LayoutLMv3ImageProcessor (UDOP model)
- upernet — SegformerImageProcessor (UPerNet model)
- van — ConvNextImageProcessor (VAN model)
- videomae — VideoMAEImageProcessor (VideoMAE model)
- vilt — ViltImageProcessor (ViLT model)
- vipllava — CLIPImageProcessor (VipLlava model)
- vit — ViTImageProcessor (ViT model)
- vit_hybrid — ViTHybridImageProcessor (ViT Hybrid model)
- vit_mae — ViTImageProcessor (ViTMAE model)
- vit_msn — ViTImageProcessor (ViTMSN model)
- vitmatte — VitMatteImageProcessor (ViTMatte model)
- xclip — CLIPImageProcessor (X-CLIP model)
- yolos — YolosImageProcessor (YOLOS model)

Passing `token=True` is required when you want to use a private model.

Examples:

    >>> from transformers import AutoImageProcessor

    >>> # Download image processor from huggingface.co and cache.
    >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")

    >>> # If image processor files are in a directory (e.g. image processor was saved using *save_pretrained('./test/saved_model/')*)
    >>> # image_processor = AutoImageProcessor.from_pretrained("./test/saved_model/")

####
register

< source >

( config_class image_processor_class exist_ok = False )

Parameters

- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
- image_processor_class (ImageProcessingMixin) — The image processor to register.

Register a new image processor for this class.

##
AutoProcessor

###
class transformers.AutoProcessor

< source >

( )

This is a generic processor class that will be instantiated as one of the processor classes of the library when created with the AutoProcessor.from_pretrained() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_pretrained

< source >

( pretrained_model_name_or_path **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — This can be either:
  - a string, the model id of a pretrained feature_extractor hosted inside a model repo on huggingface.co.
  - a path to a directory containing a processor files saved using the `save_pretrained()` method, e.g., `./my_model_directory/`.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model feature extractor should be cached if the standard cache should not be used.
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force to (re-)download the feature extractor files and override the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
- token (`str` or bool, optional) — The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- return_unused_kwargs (`bool`, optional, defaults to `False`) — If `False`, then this function returns just the final feature extractor object. If `True`, then this functions returns a `Tuple(feature_extractor, unused_kwargs)` where unused_kwargs is a dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- kwargs (`Dict[str, Any]`, optional) — The values in kwargs of any keys which are feature extractor attributes will be used to override the loaded values. Behavior concerning key/value pairs whose keys are not feature extractor attributes is controlled by the `return_unused_kwargs` keyword parameter.

Instantiate one of the processor classes of the library from a pretrained model vocabulary.

The processor class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible):

- align — AlignProcessor (ALIGN model)
- altclip — AltCLIPProcessor (AltCLIP model)
- bark — BarkProcessor (Bark model)
- blip — BlipProcessor (BLIP model)
- blip-2 — Blip2Processor (BLIP-2 model)
- bridgetower — BridgeTowerProcessor (BridgeTower model)
- chinese_clip — ChineseCLIPProcessor (Chinese-CLIP model)
- clap — ClapProcessor (CLAP model)
- clip — CLIPProcessor (CLIP model)
- clipseg — CLIPSegProcessor (CLIPSeg model)
- clvp — ClvpProcessor (CLVP model)
- flava — FlavaProcessor (FLAVA model)
- fuyu — FuyuProcessor (Fuyu model)
- git — GitProcessor (GIT model)
- groupvit — CLIPProcessor (GroupViT model)
- hubert — Wav2Vec2Processor (Hubert model)
- idefics — IdeficsProcessor (IDEFICS model)
- instructblip — InstructBlipProcessor (InstructBLIP model)
- kosmos-2 — Kosmos2Processor (KOSMOS-2 model)
- layoutlmv2 — LayoutLMv2Processor (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Processor (LayoutLMv3 model)
- llava — LlavaProcessor (LLaVa model)
- markuplm — MarkupLMProcessor (MarkupLM model)
- mctct — MCTCTProcessor (M-CTC-T model)
- mgp-str — MgpstrProcessor (MGP-STR model)
- oneformer — OneFormerProcessor (OneFormer model)
- owlv2 — Owlv2Processor (OWLv2 model)
- owlvit — OwlViTProcessor (OWL-ViT model)
- pix2struct — Pix2StructProcessor (Pix2Struct model)
- pop2piano — Pop2PianoProcessor (Pop2Piano model)
- sam — SamProcessor (SAM model)
- seamless_m4t — SeamlessM4TProcessor (SeamlessM4T model)
- sew — Wav2Vec2Processor (SEW model)
- sew-d — Wav2Vec2Processor (SEW-D model)
- siglip — SiglipProcessor (SigLIP model)
- speech_to_text — Speech2TextProcessor (Speech2Text model)
- speech_to_text_2 — Speech2Text2Processor (Speech2Text2 model)
- speecht5 — SpeechT5Processor (SpeechT5 model)
- trocr — TrOCRProcessor (TrOCR model)
- tvlt — TvltProcessor (TVLT model)
- tvp — TvpProcessor (TVP model)
- unispeech — Wav2Vec2Processor (UniSpeech model)
- unispeech-sat — Wav2Vec2Processor (UniSpeechSat model)
- vilt — ViltProcessor (ViLT model)
- vipllava — LlavaProcessor (VipLlava model)
- vision-text-dual-encoder — VisionTextDualEncoderProcessor (VisionTextDualEncoder model)
- wav2vec2 — Wav2Vec2Processor (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2Processor (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2Processor (Wav2Vec2-Conformer model)
- wavlm — Wav2Vec2Processor (WavLM model)
- whisper — WhisperProcessor (Whisper model)
- xclip — XCLIPProcessor (X-CLIP model)

Passing `token=True` is required when you want to use a private model.

Examples:

    >>> from transformers import AutoProcessor

    >>> # Download processor from huggingface.co and cache.
    >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")

    >>> # If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
    >>> # processor = AutoProcessor.from_pretrained("./test/saved_model/")

####
register

< source >

( config_class processor_class exist_ok = False )

Parameters

- config_class (PretrainedConfig) — The configuration corresponding to the model to register.
- processor_class (`FeatureExtractorMixin`) — The processor to register.

Register a new processor for this class.

##
Generic model classes

The following auto classes are available for instantiating a base model class without a specific head.

###
AutoModel

###
class transformers.AutoModel

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the base model classes of the library when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - ASTConfig configuration class: ASTModel (Audio Spectrogram Transformer model)
  - AlbertConfig configuration class: AlbertModel (ALBERT model)
  - AlignConfig configuration class: AlignModel (ALIGN model)
  - AltCLIPConfig configuration class: AltCLIPModel (AltCLIP model)
  - AutoformerConfig configuration class: AutoformerModel (Autoformer model)
  - BarkConfig configuration class: BarkModel (Bark model)
  - BartConfig configuration class: BartModel (BART model)
  - BeitConfig configuration class: BeitModel (BEiT model)
  - BertConfig configuration class: BertModel (BERT model)
  - BertGenerationConfig configuration class: BertGenerationEncoder (Bert Generation model)
  - BigBirdConfig configuration class: BigBirdModel (BigBird model)
  - BigBirdPegasusConfig configuration class: BigBirdPegasusModel (BigBird-Pegasus model)
  - BioGptConfig configuration class: BioGptModel (BioGpt model)
  - BitConfig configuration class: BitModel (BiT model)
  - BlenderbotConfig configuration class: BlenderbotModel (Blenderbot model)
  - BlenderbotSmallConfig configuration class: BlenderbotSmallModel (BlenderbotSmall model)
  - Blip2Config configuration class: Blip2Model (BLIP-2 model)
  - BlipConfig configuration class: BlipModel (BLIP model)
  - BloomConfig configuration class: BloomModel (BLOOM model)
  - BridgeTowerConfig configuration class: BridgeTowerModel (BridgeTower model)
  - BrosConfig configuration class: BrosModel (BROS model)
  - CLIPConfig configuration class: CLIPModel (CLIP model)
  - CLIPSegConfig configuration class: CLIPSegModel (CLIPSeg model)
  - CLIPVisionConfig configuration class: CLIPVisionModel (CLIPVisionModel model)
  - CTRLConfig configuration class: CTRLModel (CTRL model)
  - CamembertConfig configuration class: CamembertModel (CamemBERT model)
  - CanineConfig configuration class: CanineModel (CANINE model)
  - ChineseCLIPConfig configuration class: ChineseCLIPModel (Chinese-CLIP model)
  - ChineseCLIPVisionConfig configuration class: ChineseCLIPVisionModel (ChineseCLIPVisionModel model)
  - ClapConfig configuration class: ClapModel (CLAP model)
  - ClvpConfig configuration class: ClvpModelForConditionalGeneration (CLVP model)
  - CodeGenConfig configuration class: CodeGenModel (CodeGen model)
  - ConditionalDetrConfig configuration class: ConditionalDetrModel (Conditional DETR model)
  - ConvBertConfig configuration class: ConvBertModel (ConvBERT model)
  - ConvNextConfig configuration class: ConvNextModel (ConvNeXT model)
  - ConvNextV2Config configuration class: ConvNextV2Model (ConvNeXTV2 model)
  - CpmAntConfig configuration class: CpmAntModel (CPM-Ant model)
  - CvtConfig configuration class: CvtModel (CvT model)
  - DPRConfig configuration class: DPRQuestionEncoder (DPR model)
  - DPTConfig configuration class: DPTModel (DPT model)
  - Data2VecAudioConfig configuration class: Data2VecAudioModel (Data2VecAudio model)
  - Data2VecTextConfig configuration class: Data2VecTextModel (Data2VecText model)
  - Data2VecVisionConfig configuration class: Data2VecVisionModel (Data2VecVision model)
  - DebertaConfig configuration class: DebertaModel (DeBERTa model)
  - DebertaV2Config configuration class: DebertaV2Model (DeBERTa-v2 model)
  - DecisionTransformerConfig configuration class: DecisionTransformerModel (Decision Transformer model)
  - DeformableDetrConfig configuration class: DeformableDetrModel (Deformable DETR model)
  - DeiTConfig configuration class: DeiTModel (DeiT model)
  - DetaConfig configuration class: DetaModel (DETA model)
  - DetrConfig configuration class: DetrModel (DETR model)
  - DinatConfig configuration class: DinatModel (DiNAT model)
  - Dinov2Config configuration class: Dinov2Model (DINOv2 model)
  - DistilBertConfig configuration class: DistilBertModel (DistilBERT model)
  - DonutSwinConfig configuration class: DonutSwinModel (DonutSwin model)
  - EfficientFormerConfig configuration class: EfficientFormerModel (EfficientFormer model)
  - EfficientNetConfig configuration class: EfficientNetModel (EfficientNet model)
  - ElectraConfig configuration class: ElectraModel (ELECTRA model)
  - EncodecConfig configuration class: EncodecModel (EnCodec model)
  - ErnieConfig configuration class: ErnieModel (ERNIE model)
  - ErnieMConfig configuration class: ErnieMModel (ErnieM model)
  - EsmConfig configuration class: EsmModel (ESM model)
  - FNetConfig configuration class: FNetModel (FNet model)
  - FSMTConfig configuration class: FSMTModel (FairSeq Machine-Translation model)
  - FalconConfig configuration class: FalconModel (Falcon model)
  - FastSpeech2ConformerConfig configuration class: FastSpeech2ConformerModel (FastSpeech2Conformer model)
  - FlaubertConfig configuration class: FlaubertModel (FlauBERT model)
  - FlavaConfig configuration class: FlavaModel (FLAVA model)
  - FocalNetConfig configuration class: FocalNetModel (FocalNet model)
  - FunnelConfig configuration class: FunnelModel or FunnelBaseModel (Funnel Transformer model)
  - GLPNConfig configuration class: GLPNModel (GLPN model)
  - GPT2Config configuration class: GPT2Model (OpenAI GPT-2 model)
  - GPTBigCodeConfig configuration class: GPTBigCodeModel (GPTBigCode model)
  - GPTJConfig configuration class: GPTJModel (GPT-J model)
  - GPTNeoConfig configuration class: GPTNeoModel (GPT Neo model)
  - GPTNeoXConfig configuration class: GPTNeoXModel (GPT NeoX model)
  - GPTNeoXJapaneseConfig configuration class: GPTNeoXJapaneseModel (GPT NeoX Japanese model)
  - GPTSanJapaneseConfig configuration class: GPTSanJapaneseForConditionalGeneration (GPTSAN-japanese model)
  - GemmaConfig configuration class: GemmaModel (Gemma model)
  - GitConfig configuration class: GitModel (GIT model)
  - GraphormerConfig configuration class: GraphormerModel (Graphormer model)
  - GroupViTConfig configuration class: GroupViTModel (GroupViT model)
  - HubertConfig configuration class: HubertModel (Hubert model)
  - IBertConfig configuration class: IBertModel (I-BERT model)
  - IdeficsConfig configuration class: IdeficsModel (IDEFICS model)
  - ImageGPTConfig configuration class: ImageGPTModel (ImageGPT model)
  - InformerConfig configuration class: InformerModel (Informer model)
  - JukeboxConfig configuration class: JukeboxModel (Jukebox model)
  - Kosmos2Config configuration class: Kosmos2Model (KOSMOS-2 model)
  - LEDConfig configuration class: LEDModel (LED model)
  - LayoutLMConfig configuration class: LayoutLMModel (LayoutLM model)
  - LayoutLMv2Config configuration class: LayoutLMv2Model (LayoutLMv2 model)
  - LayoutLMv3Config configuration class: LayoutLMv3Model (LayoutLMv3 model)
  - LevitConfig configuration class: LevitModel (LeViT model)
  - LiltConfig configuration class: LiltModel (LiLT model)
  - LlamaConfig configuration class: LlamaModel (LLaMA model)
  - LongT5Config configuration class: LongT5Model (LongT5 model)
  - LongformerConfig configuration class: LongformerModel (Longformer model)
  - LukeConfig configuration class: LukeModel (LUKE model)
  - LxmertConfig configuration class: LxmertModel (LXMERT model)
  - M2M100Config configuration class: M2M100Model (M2M100 model)
  - MBartConfig configuration class: MBartModel (mBART model)
  - MCTCTConfig configuration class: MCTCTModel (M-CTC-T model)
  - MPNetConfig configuration class: MPNetModel (MPNet model)
  - MT5Config configuration class: MT5Model (MT5 model)
  - MambaConfig configuration class: MambaModel (Mamba model)
  - MarianConfig configuration class: MarianModel (Marian model)
  - MarkupLMConfig configuration class: MarkupLMModel (MarkupLM model)
  - Mask2FormerConfig configuration class: Mask2FormerModel (Mask2Former model)
  - MaskFormerConfig configuration class: MaskFormerModel (MaskFormer model)
  - `MaskFormerSwinConfig` configuration class: `MaskFormerSwinModel` (MaskFormerSwin model)
  - MegaConfig configuration class: MegaModel (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertModel (Megatron-BERT model)
  - MgpstrConfig configuration class: MgpstrForSceneTextRecognition (MGP-STR model)
  - MistralConfig configuration class: MistralModel (Mistral model)
  - MixtralConfig configuration class: MixtralModel (Mixtral model)
  - MobileBertConfig configuration class: MobileBertModel (MobileBERT model)
  - MobileNetV1Config configuration class: MobileNetV1Model (MobileNetV1 model)
  - MobileNetV2Config configuration class: MobileNetV2Model (MobileNetV2 model)
  - MobileViTConfig configuration class: MobileViTModel (MobileViT model)
  - MobileViTV2Config configuration class: MobileViTV2Model (MobileViTV2 model)
  - MptConfig configuration class: MptModel (MPT model)
  - MraConfig configuration class: MraModel (MRA model)
  - MvpConfig configuration class: MvpModel (MVP model)
  - NatConfig configuration class: NatModel (NAT model)
  - NezhaConfig configuration class: NezhaModel (Nezha model)
  - NllbMoeConfig configuration class: NllbMoeModel (NLLB-MOE model)
  - NystromformerConfig configuration class: NystromformerModel (Nyströmformer model)
  - OPTConfig configuration class: OPTModel (OPT model)
  - OneFormerConfig configuration class: OneFormerModel (OneFormer model)
  - OpenAIGPTConfig configuration class: OpenAIGPTModel (OpenAI GPT model)
  - OpenLlamaConfig configuration class: OpenLlamaModel (OpenLlama model)
  - OwlViTConfig configuration class: OwlViTModel (OWL-ViT model)
  - Owlv2Config configuration class: Owlv2Model (OWLv2 model)
  - PLBartConfig configuration class: PLBartModel (PLBart model)
  - PatchTSMixerConfig configuration class: PatchTSMixerModel (PatchTSMixer model)
  - PatchTSTConfig configuration class: PatchTSTModel (PatchTST model)
  - PegasusConfig configuration class: PegasusModel (Pegasus model)
  - PegasusXConfig configuration class: PegasusXModel (PEGASUS-X model)
  - PerceiverConfig configuration class: PerceiverModel (Perceiver model)
  - PersimmonConfig configuration class: PersimmonModel (Persimmon model)
  - PhiConfig configuration class: PhiModel (Phi model)
  - PoolFormerConfig configuration class: PoolFormerModel (PoolFormer model)
  - ProphetNetConfig configuration class: ProphetNetModel (ProphetNet model)
  - PvtConfig configuration class: PvtModel (PVT model)
  - QDQBertConfig configuration class: QDQBertModel (QDQBert model)
  - Qwen2Config configuration class: Qwen2Model (Qwen2 model)
  - ReformerConfig configuration class: ReformerModel (Reformer model)
  - RegNetConfig configuration class: RegNetModel (RegNet model)
  - RemBertConfig configuration class: RemBertModel (RemBERT model)
  - ResNetConfig configuration class: ResNetModel (ResNet model)
  - RetriBertConfig configuration class: RetriBertModel (RetriBERT model)
  - RoCBertConfig configuration class: RoCBertModel (RoCBert model)
  - RoFormerConfig configuration class: RoFormerModel (RoFormer model)
  - RobertaConfig configuration class: RobertaModel (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormModel (RoBERTa-PreLayerNorm model)
  - RwkvConfig configuration class: RwkvModel (RWKV model)
  - SEWConfig configuration class: SEWModel (SEW model)
  - SEWDConfig configuration class: SEWDModel (SEW-D model)
  - SamConfig configuration class: SamModel (SAM model)
  - SeamlessM4TConfig configuration class: SeamlessM4TModel (SeamlessM4T model)
  - SeamlessM4Tv2Config configuration class: SeamlessM4Tv2Model (SeamlessM4Tv2 model)
  - SegGptConfig configuration class: SegGptModel (SegGPT model)
  - SegformerConfig configuration class: SegformerModel (SegFormer model)
  - SiglipConfig configuration class: SiglipModel (SigLIP model)
  - SiglipVisionConfig configuration class: SiglipVisionModel (SiglipVisionModel model)
  - Speech2TextConfig configuration class: Speech2TextModel (Speech2Text model)
  - SpeechT5Config configuration class: SpeechT5Model (SpeechT5 model)
  - SplinterConfig configuration class: SplinterModel (Splinter model)
  - SqueezeBertConfig configuration class: SqueezeBertModel (SqueezeBERT model)
  - StableLmConfig configuration class: StableLmModel (StableLm model)
  - Starcoder2Config configuration class: Starcoder2Model (Starcoder2 model)
  - SwiftFormerConfig configuration class: SwiftFormerModel (SwiftFormer model)
  - Swin2SRConfig configuration class: Swin2SRModel (Swin2SR model)
  - SwinConfig configuration class: SwinModel (Swin Transformer model)
  - Swinv2Config configuration class: Swinv2Model (Swin Transformer V2 model)
  - SwitchTransformersConfig configuration class: SwitchTransformersModel (SwitchTransformers model)
  - T5Config configuration class: T5Model (T5 model)
  - TableTransformerConfig configuration class: TableTransformerModel (Table Transformer model)
  - TapasConfig configuration class: TapasModel (TAPAS model)
  - TimeSeriesTransformerConfig configuration class: TimeSeriesTransformerModel (Time Series Transformer model)
  - TimesformerConfig configuration class: TimesformerModel (TimeSformer model)
  - TimmBackboneConfig configuration class: TimmBackbone (TimmBackbone model)
  - TrajectoryTransformerConfig configuration class: TrajectoryTransformerModel (Trajectory Transformer model)
  - TransfoXLConfig configuration class: TransfoXLModel (Transformer-XL model)
  - TvltConfig configuration class: TvltModel (TVLT model)
  - TvpConfig configuration class: TvpModel (TVP model)
  - UMT5Config configuration class: UMT5Model (UMT5 model)
  - UdopConfig configuration class: UdopModel (UDOP model)
  - UniSpeechConfig configuration class: UniSpeechModel (UniSpeech model)
  - UniSpeechSatConfig configuration class: UniSpeechSatModel (UniSpeechSat model)
  - UnivNetConfig configuration class: UnivNetModel (UnivNet model)
  - VanConfig configuration class: VanModel (VAN model)
  - ViTConfig configuration class: ViTModel (ViT model)
  - ViTHybridConfig configuration class: ViTHybridModel (ViT Hybrid model)
  - ViTMAEConfig configuration class: ViTMAEModel (ViTMAE model)
  - ViTMSNConfig configuration class: ViTMSNModel (ViTMSN model)
  - VideoMAEConfig configuration class: VideoMAEModel (VideoMAE model)
  - ViltConfig configuration class: ViltModel (ViLT model)
  - VisionTextDualEncoderConfig configuration class: VisionTextDualEncoderModel (VisionTextDualEncoder model)
  - VisualBertConfig configuration class: VisualBertModel (VisualBERT model)
  - VitDetConfig configuration class: VitDetModel (VitDet model)
  - VitsConfig configuration class: VitsModel (VITS model)
  - VivitConfig configuration class: VivitModel (ViViT model)
  - Wav2Vec2BertConfig configuration class: Wav2Vec2BertModel (Wav2Vec2-BERT model)
  - Wav2Vec2Config configuration class: Wav2Vec2Model (Wav2Vec2 model)
  - Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerModel (Wav2Vec2-Conformer model)
  - WavLMConfig configuration class: WavLMModel (WavLM model)
  - WhisperConfig configuration class: WhisperModel (Whisper model)
  - XCLIPConfig configuration class: XCLIPModel (X-CLIP model)
  - XGLMConfig configuration class: XGLMModel (XGLM model)
  - XLMConfig configuration class: XLMModel (XLM model)
  - XLMProphetNetConfig configuration class: XLMProphetNetModel (XLM-ProphetNet model)
  - XLMRobertaConfig configuration class: XLMRobertaModel (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLModel (XLM-RoBERTa-XL model)
  - XLNetConfig configuration class: XLNetModel (XLNet model)
  - XmodConfig configuration class: XmodModel (X-MOD model)
  - YolosConfig configuration class: YolosModel (YOLOS model)
  - YosoConfig configuration class: YosoModel (YOSO model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the base model classes of the library from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModel

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModel.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the base model classes of the library from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertModel (ALBERT model)
- align — AlignModel (ALIGN model)
- altclip — AltCLIPModel (AltCLIP model)
- audio-spectrogram-transformer — ASTModel (Audio Spectrogram Transformer model)
- autoformer — AutoformerModel (Autoformer model)
- bark — BarkModel (Bark model)
- bart — BartModel (BART model)
- beit — BeitModel (BEiT model)
- bert — BertModel (BERT model)
- bert-generation — BertGenerationEncoder (Bert Generation model)
- big_bird — BigBirdModel (BigBird model)
- bigbird_pegasus — BigBirdPegasusModel (BigBird-Pegasus model)
- biogpt — BioGptModel (BioGpt model)
- bit — BitModel (BiT model)
- blenderbot — BlenderbotModel (Blenderbot model)
- blenderbot-small — BlenderbotSmallModel (BlenderbotSmall model)
- blip — BlipModel (BLIP model)
- blip-2 — Blip2Model (BLIP-2 model)
- bloom — BloomModel (BLOOM model)
- bridgetower — BridgeTowerModel (BridgeTower model)
- bros — BrosModel (BROS model)
- camembert — CamembertModel (CamemBERT model)
- canine — CanineModel (CANINE model)
- chinese_clip — ChineseCLIPModel (Chinese-CLIP model)
- chinese_clip_vision_model — ChineseCLIPVisionModel (ChineseCLIPVisionModel model)
- clap — ClapModel (CLAP model)
- clip — CLIPModel (CLIP model)
- clip_vision_model — CLIPVisionModel (CLIPVisionModel model)
- clipseg — CLIPSegModel (CLIPSeg model)
- clvp — ClvpModelForConditionalGeneration (CLVP model)
- code_llama — LlamaModel (CodeLlama model)
- codegen — CodeGenModel (CodeGen model)
- conditional_detr — ConditionalDetrModel (Conditional DETR model)
- convbert — ConvBertModel (ConvBERT model)
- convnext — ConvNextModel (ConvNeXT model)
- convnextv2 — ConvNextV2Model (ConvNeXTV2 model)
- cpmant — CpmAntModel (CPM-Ant model)
- ctrl — CTRLModel (CTRL model)
- cvt — CvtModel (CvT model)
- data2vec-audio — Data2VecAudioModel (Data2VecAudio model)
- data2vec-text — Data2VecTextModel (Data2VecText model)
- data2vec-vision — Data2VecVisionModel (Data2VecVision model)
- deberta — DebertaModel (DeBERTa model)
- deberta-v2 — DebertaV2Model (DeBERTa-v2 model)
- decision_transformer — DecisionTransformerModel (Decision Transformer model)
- deformable_detr — DeformableDetrModel (Deformable DETR model)
- deit — DeiTModel (DeiT model)
- deta — DetaModel (DETA model)
- detr — DetrModel (DETR model)
- dinat — DinatModel (DiNAT model)
- dinov2 — Dinov2Model (DINOv2 model)
- distilbert — DistilBertModel (DistilBERT model)
- donut-swin — DonutSwinModel (DonutSwin model)
- dpr — DPRQuestionEncoder (DPR model)
- dpt — DPTModel (DPT model)
- efficientformer — EfficientFormerModel (EfficientFormer model)
- efficientnet — EfficientNetModel (EfficientNet model)
- electra — ElectraModel (ELECTRA model)
- encodec — EncodecModel (EnCodec model)
- ernie — ErnieModel (ERNIE model)
- ernie_m — ErnieMModel (ErnieM model)
- esm — EsmModel (ESM model)
- falcon — FalconModel (Falcon model)
- fastspeech2_conformer — FastSpeech2ConformerModel (FastSpeech2Conformer model)
- flaubert — FlaubertModel (FlauBERT model)
- flava — FlavaModel (FLAVA model)
- fnet — FNetModel (FNet model)
- focalnet — FocalNetModel (FocalNet model)
- fsmt — FSMTModel (FairSeq Machine-Translation model)
- funnel — FunnelModel or FunnelBaseModel (Funnel Transformer model)
- gemma — GemmaModel (Gemma model)
- git — GitModel (GIT model)
- glpn — GLPNModel (GLPN model)
- gpt-sw3 — GPT2Model (GPT-Sw3 model)
- gpt2 — GPT2Model (OpenAI GPT-2 model)
- gpt_bigcode — GPTBigCodeModel (GPTBigCode model)
- gpt_neo — GPTNeoModel (GPT Neo model)
- gpt_neox — GPTNeoXModel (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseModel (GPT NeoX Japanese model)
- gptj — GPTJModel (GPT-J model)
- gptsan-japanese — GPTSanJapaneseForConditionalGeneration (GPTSAN-japanese model)
- graphormer — GraphormerModel (Graphormer model)
- groupvit — GroupViTModel (GroupViT model)
- hubert — HubertModel (Hubert model)
- ibert — IBertModel (I-BERT model)
- idefics — IdeficsModel (IDEFICS model)
- imagegpt — ImageGPTModel (ImageGPT model)
- informer — InformerModel (Informer model)
- jukebox — JukeboxModel (Jukebox model)
- kosmos-2 — Kosmos2Model (KOSMOS-2 model)
- layoutlm — LayoutLMModel (LayoutLM model)
- layoutlmv2 — LayoutLMv2Model (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3Model (LayoutLMv3 model)
- led — LEDModel (LED model)
- levit — LevitModel (LeViT model)
- lilt — LiltModel (LiLT model)
- llama — LlamaModel (LLaMA model)
- longformer — LongformerModel (Longformer model)
- longt5 — LongT5Model (LongT5 model)
- luke — LukeModel (LUKE model)
- lxmert — LxmertModel (LXMERT model)
- m2m_100 — M2M100Model (M2M100 model)
- mamba — MambaModel (Mamba model)
- marian — MarianModel (Marian model)
- markuplm — MarkupLMModel (MarkupLM model)
- mask2former — Mask2FormerModel (Mask2Former model)
- maskformer — MaskFormerModel (MaskFormer model)
- maskformer-swin — `MaskFormerSwinModel` (MaskFormerSwin model)
- mbart — MBartModel (mBART model)
- mctct — MCTCTModel (M-CTC-T model)
- mega — MegaModel (MEGA model)
- megatron-bert — MegatronBertModel (Megatron-BERT model)
- mgp-str — MgpstrForSceneTextRecognition (MGP-STR model)
- mistral — MistralModel (Mistral model)
- mixtral — MixtralModel (Mixtral model)
- mobilebert — MobileBertModel (MobileBERT model)
- mobilenet_v1 — MobileNetV1Model (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2Model (MobileNetV2 model)
- mobilevit — MobileViTModel (MobileViT model)
- mobilevitv2 — MobileViTV2Model (MobileViTV2 model)
- mpnet — MPNetModel (MPNet model)
- mpt — MptModel (MPT model)
- mra — MraModel (MRA model)
- mt5 — MT5Model (MT5 model)
- mvp — MvpModel (MVP model)
- nat — NatModel (NAT model)
- nezha — NezhaModel (Nezha model)
- nllb-moe — NllbMoeModel (NLLB-MOE model)
- nystromformer — NystromformerModel (Nyströmformer model)
- oneformer — OneFormerModel (OneFormer model)
- open-llama — OpenLlamaModel (OpenLlama model)
- openai-gpt — OpenAIGPTModel (OpenAI GPT model)
- opt — OPTModel (OPT model)
- owlv2 — Owlv2Model (OWLv2 model)
- owlvit — OwlViTModel (OWL-ViT model)
- patchtsmixer — PatchTSMixerModel (PatchTSMixer model)
- patchtst — PatchTSTModel (PatchTST model)
- pegasus — PegasusModel (Pegasus model)
- pegasus_x — PegasusXModel (PEGASUS-X model)
- perceiver — PerceiverModel (Perceiver model)
- persimmon — PersimmonModel (Persimmon model)
- phi — PhiModel (Phi model)
- plbart — PLBartModel (PLBart model)
- poolformer — PoolFormerModel (PoolFormer model)
- prophetnet — ProphetNetModel (ProphetNet model)
- pvt — PvtModel (PVT model)
- qdqbert — QDQBertModel (QDQBert model)
- qwen2 — Qwen2Model (Qwen2 model)
- reformer — ReformerModel (Reformer model)
- regnet — RegNetModel (RegNet model)
- rembert — RemBertModel (RemBERT model)
- resnet — ResNetModel (ResNet model)
- retribert — RetriBertModel (RetriBERT model)
- roberta — RobertaModel (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormModel (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertModel (RoCBert model)
- roformer — RoFormerModel (RoFormer model)
- rwkv — RwkvModel (RWKV model)
- sam — SamModel (SAM model)
- seamless_m4t — SeamlessM4TModel (SeamlessM4T model)
- seamless_m4t_v2 — SeamlessM4Tv2Model (SeamlessM4Tv2 model)
- segformer — SegformerModel (SegFormer model)
- seggpt — SegGptModel (SegGPT model)
- sew — SEWModel (SEW model)
- sew-d — SEWDModel (SEW-D model)
- siglip — SiglipModel (SigLIP model)
- siglip_vision_model — SiglipVisionModel (SiglipVisionModel model)
- speech_to_text — Speech2TextModel (Speech2Text model)
- speecht5 — SpeechT5Model (SpeechT5 model)
- splinter — SplinterModel (Splinter model)
- squeezebert — SqueezeBertModel (SqueezeBERT model)
- stablelm — StableLmModel (StableLm model)
- starcoder2 — Starcoder2Model (Starcoder2 model)
- swiftformer — SwiftFormerModel (SwiftFormer model)
- swin — SwinModel (Swin Transformer model)
- swin2sr — Swin2SRModel (Swin2SR model)
- swinv2 — Swinv2Model (Swin Transformer V2 model)
- switch_transformers — SwitchTransformersModel (SwitchTransformers model)
- t5 — T5Model (T5 model)
- table-transformer — TableTransformerModel (Table Transformer model)
- tapas — TapasModel (TAPAS model)
- time_series_transformer — TimeSeriesTransformerModel (Time Series Transformer model)
- timesformer — TimesformerModel (TimeSformer model)
- timm_backbone — TimmBackbone (TimmBackbone model)
- trajectory_transformer — TrajectoryTransformerModel (Trajectory Transformer model)
- transfo-xl — TransfoXLModel (Transformer-XL model)
- tvlt — TvltModel (TVLT model)
- tvp — TvpModel (TVP model)
- udop — UdopModel (UDOP model)
- umt5 — UMT5Model (UMT5 model)
- unispeech — UniSpeechModel (UniSpeech model)
- unispeech-sat — UniSpeechSatModel (UniSpeechSat model)
- univnet — UnivNetModel (UnivNet model)
- van — VanModel (VAN model)
- videomae — VideoMAEModel (VideoMAE model)
- vilt — ViltModel (ViLT model)
- vision-text-dual-encoder — VisionTextDualEncoderModel (VisionTextDualEncoder model)
- visual_bert — VisualBertModel (VisualBERT model)
- vit — ViTModel (ViT model)
- vit_hybrid — ViTHybridModel (ViT Hybrid model)
- vit_mae — ViTMAEModel (ViTMAE model)
- vit_msn — ViTMSNModel (ViTMSN model)
- vitdet — VitDetModel (VitDet model)
- vits — VitsModel (VITS model)
- vivit — VivitModel (ViViT model)
- wav2vec2 — Wav2Vec2Model (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2BertModel (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2ConformerModel (Wav2Vec2-Conformer model)
- wavlm — WavLMModel (WavLM model)
- whisper — WhisperModel (Whisper model)
- xclip — XCLIPModel (X-CLIP model)
- xglm — XGLMModel (XGLM model)
- xlm — XLMModel (XLM model)
- xlm-prophetnet — XLMProphetNetModel (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaModel (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLModel (XLM-RoBERTa-XL model)
- xlnet — XLNetModel (XLNet model)
- xmod — XmodModel (X-MOD model)
- yolos — YolosModel (YOLOS model)
- yoso — YosoModel (YOSO model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModel

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModel.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModel.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModel.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModel

###
class transformers.TFAutoModel

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the base model classes of the library when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: TFAlbertModel (ALBERT model)
  - BartConfig configuration class: TFBartModel (BART model)
  - BertConfig configuration class: TFBertModel (BERT model)
  - BlenderbotConfig configuration class: TFBlenderbotModel (Blenderbot model)
  - BlenderbotSmallConfig configuration class: TFBlenderbotSmallModel (BlenderbotSmall model)
  - BlipConfig configuration class: TFBlipModel (BLIP model)
  - CLIPConfig configuration class: TFCLIPModel (CLIP model)
  - CTRLConfig configuration class: TFCTRLModel (CTRL model)
  - CamembertConfig configuration class: TFCamembertModel (CamemBERT model)
  - ConvBertConfig configuration class: TFConvBertModel (ConvBERT model)
  - ConvNextConfig configuration class: TFConvNextModel (ConvNeXT model)
  - ConvNextV2Config configuration class: TFConvNextV2Model (ConvNeXTV2 model)
  - CvtConfig configuration class: TFCvtModel (CvT model)
  - DPRConfig configuration class: TFDPRQuestionEncoder (DPR model)
  - Data2VecVisionConfig configuration class: TFData2VecVisionModel (Data2VecVision model)
  - DebertaConfig configuration class: TFDebertaModel (DeBERTa model)
  - DebertaV2Config configuration class: TFDebertaV2Model (DeBERTa-v2 model)
  - DeiTConfig configuration class: TFDeiTModel (DeiT model)
  - DistilBertConfig configuration class: TFDistilBertModel (DistilBERT model)
  - EfficientFormerConfig configuration class: TFEfficientFormerModel (EfficientFormer model)
  - ElectraConfig configuration class: TFElectraModel (ELECTRA model)
  - EsmConfig configuration class: TFEsmModel (ESM model)
  - FlaubertConfig configuration class: TFFlaubertModel (FlauBERT model)
  - FunnelConfig configuration class: TFFunnelModel or TFFunnelBaseModel (Funnel Transformer model)
  - GPT2Config configuration class: TFGPT2Model (OpenAI GPT-2 model)
  - GPTJConfig configuration class: TFGPTJModel (GPT-J model)
  - GroupViTConfig configuration class: TFGroupViTModel (GroupViT model)
  - HubertConfig configuration class: TFHubertModel (Hubert model)
  - LEDConfig configuration class: TFLEDModel (LED model)
  - LayoutLMConfig configuration class: TFLayoutLMModel (LayoutLM model)
  - LayoutLMv3Config configuration class: TFLayoutLMv3Model (LayoutLMv3 model)
  - LongformerConfig configuration class: TFLongformerModel (Longformer model)
  - LxmertConfig configuration class: TFLxmertModel (LXMERT model)
  - MBartConfig configuration class: TFMBartModel (mBART model)
  - MPNetConfig configuration class: TFMPNetModel (MPNet model)
  - MT5Config configuration class: TFMT5Model (MT5 model)
  - MarianConfig configuration class: TFMarianModel (Marian model)
  - MobileBertConfig configuration class: TFMobileBertModel (MobileBERT model)
  - MobileViTConfig configuration class: TFMobileViTModel (MobileViT model)
  - OPTConfig configuration class: TFOPTModel (OPT model)
  - OpenAIGPTConfig configuration class: TFOpenAIGPTModel (OpenAI GPT model)
  - PegasusConfig configuration class: TFPegasusModel (Pegasus model)
  - RegNetConfig configuration class: TFRegNetModel (RegNet model)
  - RemBertConfig configuration class: TFRemBertModel (RemBERT model)
  - ResNetConfig configuration class: TFResNetModel (ResNet model)
  - RoFormerConfig configuration class: TFRoFormerModel (RoFormer model)
  - RobertaConfig configuration class: TFRobertaModel (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormModel (RoBERTa-PreLayerNorm model)
  - SamConfig configuration class: TFSamModel (SAM model)
  - SegformerConfig configuration class: TFSegformerModel (SegFormer model)
  - Speech2TextConfig configuration class: TFSpeech2TextModel (Speech2Text model)
  - SwinConfig configuration class: TFSwinModel (Swin Transformer model)
  - T5Config configuration class: TFT5Model (T5 model)
  - TapasConfig configuration class: TFTapasModel (TAPAS model)
  - TransfoXLConfig configuration class: TFTransfoXLModel (Transformer-XL model)
  - ViTConfig configuration class: TFViTModel (ViT model)
  - ViTMAEConfig configuration class: TFViTMAEModel (ViTMAE model)
  - VisionTextDualEncoderConfig configuration class: TFVisionTextDualEncoderModel (VisionTextDualEncoder model)
  - Wav2Vec2Config configuration class: TFWav2Vec2Model (Wav2Vec2 model)
  - WhisperConfig configuration class: TFWhisperModel (Whisper model)
  - XGLMConfig configuration class: TFXGLMModel (XGLM model)
  - XLMConfig configuration class: TFXLMModel (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaModel (XLM-RoBERTa model)
  - XLNetConfig configuration class: TFXLNetModel (XLNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the base model classes of the library from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModel

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModel.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the base model classes of the library from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — TFAlbertModel (ALBERT model)
- bart — TFBartModel (BART model)
- bert — TFBertModel (BERT model)
- blenderbot — TFBlenderbotModel (Blenderbot model)
- blenderbot-small — TFBlenderbotSmallModel (BlenderbotSmall model)
- blip — TFBlipModel (BLIP model)
- camembert — TFCamembertModel (CamemBERT model)
- clip — TFCLIPModel (CLIP model)
- convbert — TFConvBertModel (ConvBERT model)
- convnext — TFConvNextModel (ConvNeXT model)
- convnextv2 — TFConvNextV2Model (ConvNeXTV2 model)
- ctrl — TFCTRLModel (CTRL model)
- cvt — TFCvtModel (CvT model)
- data2vec-vision — TFData2VecVisionModel (Data2VecVision model)
- deberta — TFDebertaModel (DeBERTa model)
- deberta-v2 — TFDebertaV2Model (DeBERTa-v2 model)
- deit — TFDeiTModel (DeiT model)
- distilbert — TFDistilBertModel (DistilBERT model)
- dpr — TFDPRQuestionEncoder (DPR model)
- efficientformer — TFEfficientFormerModel (EfficientFormer model)
- electra — TFElectraModel (ELECTRA model)
- esm — TFEsmModel (ESM model)
- flaubert — TFFlaubertModel (FlauBERT model)
- funnel — TFFunnelModel or TFFunnelBaseModel (Funnel Transformer model)
- gpt-sw3 — TFGPT2Model (GPT-Sw3 model)
- gpt2 — TFGPT2Model (OpenAI GPT-2 model)
- gptj — TFGPTJModel (GPT-J model)
- groupvit — TFGroupViTModel (GroupViT model)
- hubert — TFHubertModel (Hubert model)
- layoutlm — TFLayoutLMModel (LayoutLM model)
- layoutlmv3 — TFLayoutLMv3Model (LayoutLMv3 model)
- led — TFLEDModel (LED model)
- longformer — TFLongformerModel (Longformer model)
- lxmert — TFLxmertModel (LXMERT model)
- marian — TFMarianModel (Marian model)
- mbart — TFMBartModel (mBART model)
- mobilebert — TFMobileBertModel (MobileBERT model)
- mobilevit — TFMobileViTModel (MobileViT model)
- mpnet — TFMPNetModel (MPNet model)
- mt5 — TFMT5Model (MT5 model)
- openai-gpt — TFOpenAIGPTModel (OpenAI GPT model)
- opt — TFOPTModel (OPT model)
- pegasus — TFPegasusModel (Pegasus model)
- regnet — TFRegNetModel (RegNet model)
- rembert — TFRemBertModel (RemBERT model)
- resnet — TFResNetModel (ResNet model)
- roberta — TFRobertaModel (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormModel (RoBERTa-PreLayerNorm model)
- roformer — TFRoFormerModel (RoFormer model)
- sam — TFSamModel (SAM model)
- segformer — TFSegformerModel (SegFormer model)
- speech_to_text — TFSpeech2TextModel (Speech2Text model)
- swin — TFSwinModel (Swin Transformer model)
- t5 — TFT5Model (T5 model)
- tapas — TFTapasModel (TAPAS model)
- transfo-xl — TFTransfoXLModel (Transformer-XL model)
- vision-text-dual-encoder — TFVisionTextDualEncoderModel (VisionTextDualEncoder model)
- vit — TFViTModel (ViT model)
- vit_mae — TFViTMAEModel (ViTMAE model)
- wav2vec2 — TFWav2Vec2Model (Wav2Vec2 model)
- whisper — TFWhisperModel (Whisper model)
- xglm — TFXGLMModel (XGLM model)
- xlm — TFXLMModel (XLM model)
- xlm-roberta — TFXLMRobertaModel (XLM-RoBERTa model)
- xlnet — TFXLNetModel (XLNet model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModel

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModel.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModel.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModel

###
class transformers.FlaxAutoModel

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the base model classes of the library when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: FlaxAlbertModel (ALBERT model)
  - BartConfig configuration class: FlaxBartModel (BART model)
  - BeitConfig configuration class: FlaxBeitModel (BEiT model)
  - BertConfig configuration class: FlaxBertModel (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdModel (BigBird model)
  - BlenderbotConfig configuration class: FlaxBlenderbotModel (Blenderbot model)
  - BlenderbotSmallConfig configuration class: FlaxBlenderbotSmallModel (BlenderbotSmall model)
  - BloomConfig configuration class: FlaxBloomModel (BLOOM model)
  - CLIPConfig configuration class: FlaxCLIPModel (CLIP model)
  - DistilBertConfig configuration class: FlaxDistilBertModel (DistilBERT model)
  - ElectraConfig configuration class: FlaxElectraModel (ELECTRA model)
  - GPT2Config configuration class: FlaxGPT2Model (OpenAI GPT-2 model)
  - GPTJConfig configuration class: FlaxGPTJModel (GPT-J model)
  - GPTNeoConfig configuration class: FlaxGPTNeoModel (GPT Neo model)
  - GemmaConfig configuration class: FlaxGemmaModel (Gemma model)
  - LlamaConfig configuration class: FlaxLlamaModel (LLaMA model)
  - LongT5Config configuration class: FlaxLongT5Model (LongT5 model)
  - MBartConfig configuration class: FlaxMBartModel (mBART model)
  - MT5Config configuration class: FlaxMT5Model (MT5 model)
  - MarianConfig configuration class: FlaxMarianModel (Marian model)
  - MistralConfig configuration class: FlaxMistralModel (Mistral model)
  - OPTConfig configuration class: FlaxOPTModel (OPT model)
  - PegasusConfig configuration class: FlaxPegasusModel (Pegasus model)
  - RegNetConfig configuration class: FlaxRegNetModel (RegNet model)
  - ResNetConfig configuration class: FlaxResNetModel (ResNet model)
  - RoFormerConfig configuration class: FlaxRoFormerModel (RoFormer model)
  - RobertaConfig configuration class: FlaxRobertaModel (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormModel (RoBERTa-PreLayerNorm model)
  - T5Config configuration class: FlaxT5Model (T5 model)
  - ViTConfig configuration class: FlaxViTModel (ViT model)
  - VisionTextDualEncoderConfig configuration class: FlaxVisionTextDualEncoderModel (VisionTextDualEncoder model)
  - Wav2Vec2Config configuration class: FlaxWav2Vec2Model (Wav2Vec2 model)
  - WhisperConfig configuration class: FlaxWhisperModel (Whisper model)
  - XGLMConfig configuration class: FlaxXGLMModel (XGLM model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaModel (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the base model classes of the library from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModel

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModel.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the base model classes of the library from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — FlaxAlbertModel (ALBERT model)
- bart — FlaxBartModel (BART model)
- beit — FlaxBeitModel (BEiT model)
- bert — FlaxBertModel (BERT model)
- big_bird — FlaxBigBirdModel (BigBird model)
- blenderbot — FlaxBlenderbotModel (Blenderbot model)
- blenderbot-small — FlaxBlenderbotSmallModel (BlenderbotSmall model)
- bloom — FlaxBloomModel (BLOOM model)
- clip — FlaxCLIPModel (CLIP model)
- distilbert — FlaxDistilBertModel (DistilBERT model)
- electra — FlaxElectraModel (ELECTRA model)
- gemma — FlaxGemmaModel (Gemma model)
- gpt-sw3 — FlaxGPT2Model (GPT-Sw3 model)
- gpt2 — FlaxGPT2Model (OpenAI GPT-2 model)
- gpt_neo — FlaxGPTNeoModel (GPT Neo model)
- gptj — FlaxGPTJModel (GPT-J model)
- llama — FlaxLlamaModel (LLaMA model)
- longt5 — FlaxLongT5Model (LongT5 model)
- marian — FlaxMarianModel (Marian model)
- mbart — FlaxMBartModel (mBART model)
- mistral — FlaxMistralModel (Mistral model)
- mt5 — FlaxMT5Model (MT5 model)
- opt — FlaxOPTModel (OPT model)
- pegasus — FlaxPegasusModel (Pegasus model)
- regnet — FlaxRegNetModel (RegNet model)
- resnet — FlaxResNetModel (ResNet model)
- roberta — FlaxRobertaModel (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormModel (RoBERTa-PreLayerNorm model)
- roformer — FlaxRoFormerModel (RoFormer model)
- t5 — FlaxT5Model (T5 model)
- vision-text-dual-encoder — FlaxVisionTextDualEncoderModel (VisionTextDualEncoder model)
- vit — FlaxViTModel (ViT model)
- wav2vec2 — FlaxWav2Vec2Model (Wav2Vec2 model)
- whisper — FlaxWhisperModel (Whisper model)
- xglm — FlaxXGLMModel (XGLM model)
- xlm-roberta — FlaxXLMRobertaModel (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModel

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModel.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModel.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModel.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

##
Generic pretraining classes

The following auto classes are available for instantiating a model with a pretraining head.

###
AutoModelForPreTraining

###
class transformers.AutoModelForPreTraining

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: AlbertForPreTraining (ALBERT model)
  - BartConfig configuration class: BartForConditionalGeneration (BART model)
  - BertConfig configuration class: BertForPreTraining (BERT model)
  - BigBirdConfig configuration class: BigBirdForPreTraining (BigBird model)
  - BloomConfig configuration class: BloomForCausalLM (BLOOM model)
  - CTRLConfig configuration class: CTRLLMHeadModel (CTRL model)
  - CamembertConfig configuration class: CamembertForMaskedLM (CamemBERT model)
  - Data2VecTextConfig configuration class: Data2VecTextForMaskedLM (Data2VecText model)
  - DebertaConfig configuration class: DebertaForMaskedLM (DeBERTa model)
  - DebertaV2Config configuration class: DebertaV2ForMaskedLM (DeBERTa-v2 model)
  - DistilBertConfig configuration class: DistilBertForMaskedLM (DistilBERT model)
  - ElectraConfig configuration class: ElectraForPreTraining (ELECTRA model)
  - ErnieConfig configuration class: ErnieForPreTraining (ERNIE model)
  - FNetConfig configuration class: FNetForPreTraining (FNet model)
  - FSMTConfig configuration class: FSMTForConditionalGeneration (FairSeq Machine-Translation model)
  - FlaubertConfig configuration class: FlaubertWithLMHeadModel (FlauBERT model)
  - FlavaConfig configuration class: FlavaForPreTraining (FLAVA model)
  - FunnelConfig configuration class: FunnelForPreTraining (Funnel Transformer model)
  - GPT2Config configuration class: GPT2LMHeadModel (OpenAI GPT-2 model)
  - GPTBigCodeConfig configuration class: GPTBigCodeForCausalLM (GPTBigCode model)
  - GPTSanJapaneseConfig configuration class: GPTSanJapaneseForConditionalGeneration (GPTSAN-japanese model)
  - IBertConfig configuration class: IBertForMaskedLM (I-BERT model)
  - IdeficsConfig configuration class: IdeficsForVisionText2Text (IDEFICS model)
  - LayoutLMConfig configuration class: LayoutLMForMaskedLM (LayoutLM model)
  - LlavaConfig configuration class: LlavaForConditionalGeneration (LLaVa model)
  - LongformerConfig configuration class: LongformerForMaskedLM (Longformer model)
  - LukeConfig configuration class: LukeForMaskedLM (LUKE model)
  - LxmertConfig configuration class: LxmertForPreTraining (LXMERT model)
  - MPNetConfig configuration class: MPNetForMaskedLM (MPNet model)
  - MambaConfig configuration class: MambaForCausalLM (Mamba model)
  - MegaConfig configuration class: MegaForMaskedLM (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertForPreTraining (Megatron-BERT model)
  - MobileBertConfig configuration class: MobileBertForPreTraining (MobileBERT model)
  - MptConfig configuration class: MptForCausalLM (MPT model)
  - MraConfig configuration class: MraForMaskedLM (MRA model)
  - MvpConfig configuration class: MvpForConditionalGeneration (MVP model)
  - NezhaConfig configuration class: NezhaForPreTraining (Nezha model)
  - NllbMoeConfig configuration class: NllbMoeForConditionalGeneration (NLLB-MOE model)
  - OpenAIGPTConfig configuration class: OpenAIGPTLMHeadModel (OpenAI GPT model)
  - RetriBertConfig configuration class: RetriBertModel (RetriBERT model)
  - RoCBertConfig configuration class: RoCBertForPreTraining (RoCBert model)
  - RobertaConfig configuration class: RobertaForMaskedLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
  - RwkvConfig configuration class: RwkvForCausalLM (RWKV model)
  - SplinterConfig configuration class: SplinterForPreTraining (Splinter model)
  - SqueezeBertConfig configuration class: SqueezeBertForMaskedLM (SqueezeBERT model)
  - SwitchTransformersConfig configuration class: SwitchTransformersForConditionalGeneration (SwitchTransformers model)
  - T5Config configuration class: T5ForConditionalGeneration (T5 model)
  - TapasConfig configuration class: TapasForMaskedLM (TAPAS model)
  - TransfoXLConfig configuration class: TransfoXLLMHeadModel (Transformer-XL model)
  - TvltConfig configuration class: TvltForPreTraining (TVLT model)
  - UniSpeechConfig configuration class: UniSpeechForPreTraining (UniSpeech model)
  - UniSpeechSatConfig configuration class: UniSpeechSatForPreTraining (UniSpeechSat model)
  - ViTMAEConfig configuration class: ViTMAEForPreTraining (ViTMAE model)
  - VideoMAEConfig configuration class: VideoMAEForPreTraining (VideoMAE model)
  - VipLlavaConfig configuration class: VipLlavaForConditionalGeneration (VipLlava model)
  - VisualBertConfig configuration class: VisualBertForPreTraining (VisualBERT model)
  - Wav2Vec2Config configuration class: Wav2Vec2ForPreTraining (Wav2Vec2 model)
  - Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForPreTraining (Wav2Vec2-Conformer model)
  - XLMConfig configuration class: XLMWithLMHeadModel (XLM model)
  - XLMRobertaConfig configuration class: XLMRobertaForMaskedLM (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLForMaskedLM (XLM-RoBERTa-XL model)
  - XLNetConfig configuration class: XLNetLMHeadModel (XLNet model)
  - XmodConfig configuration class: XmodForMaskedLM (X-MOD model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a pretraining head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForPreTraining

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForPreTraining.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertForPreTraining (ALBERT model)
- bart — BartForConditionalGeneration (BART model)
- bert — BertForPreTraining (BERT model)
- big_bird — BigBirdForPreTraining (BigBird model)
- bloom — BloomForCausalLM (BLOOM model)
- camembert — CamembertForMaskedLM (CamemBERT model)
- ctrl — CTRLLMHeadModel (CTRL model)
- data2vec-text — Data2VecTextForMaskedLM (Data2VecText model)
- deberta — DebertaForMaskedLM (DeBERTa model)
- deberta-v2 — DebertaV2ForMaskedLM (DeBERTa-v2 model)
- distilbert — DistilBertForMaskedLM (DistilBERT model)
- electra — ElectraForPreTraining (ELECTRA model)
- ernie — ErnieForPreTraining (ERNIE model)
- flaubert — FlaubertWithLMHeadModel (FlauBERT model)
- flava — FlavaForPreTraining (FLAVA model)
- fnet — FNetForPreTraining (FNet model)
- fsmt — FSMTForConditionalGeneration (FairSeq Machine-Translation model)
- funnel — FunnelForPreTraining (Funnel Transformer model)
- gpt-sw3 — GPT2LMHeadModel (GPT-Sw3 model)
- gpt2 — GPT2LMHeadModel (OpenAI GPT-2 model)
- gpt_bigcode — GPTBigCodeForCausalLM (GPTBigCode model)
- gptsan-japanese — GPTSanJapaneseForConditionalGeneration (GPTSAN-japanese model)
- ibert — IBertForMaskedLM (I-BERT model)
- idefics — IdeficsForVisionText2Text (IDEFICS model)
- layoutlm — LayoutLMForMaskedLM (LayoutLM model)
- llava — LlavaForConditionalGeneration (LLaVa model)
- longformer — LongformerForMaskedLM (Longformer model)
- luke — LukeForMaskedLM (LUKE model)
- lxmert — LxmertForPreTraining (LXMERT model)
- mamba — MambaForCausalLM (Mamba model)
- mega — MegaForMaskedLM (MEGA model)
- megatron-bert — MegatronBertForPreTraining (Megatron-BERT model)
- mobilebert — MobileBertForPreTraining (MobileBERT model)
- mpnet — MPNetForMaskedLM (MPNet model)
- mpt — MptForCausalLM (MPT model)
- mra — MraForMaskedLM (MRA model)
- mvp — MvpForConditionalGeneration (MVP model)
- nezha — NezhaForPreTraining (Nezha model)
- nllb-moe — NllbMoeForConditionalGeneration (NLLB-MOE model)
- openai-gpt — OpenAIGPTLMHeadModel (OpenAI GPT model)
- retribert — RetriBertModel (RetriBERT model)
- roberta — RobertaForMaskedLM (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertForPreTraining (RoCBert model)
- rwkv — RwkvForCausalLM (RWKV model)
- splinter — SplinterForPreTraining (Splinter model)
- squeezebert — SqueezeBertForMaskedLM (SqueezeBERT model)
- switch_transformers — SwitchTransformersForConditionalGeneration (SwitchTransformers model)
- t5 — T5ForConditionalGeneration (T5 model)
- tapas — TapasForMaskedLM (TAPAS model)
- transfo-xl — TransfoXLLMHeadModel (Transformer-XL model)
- tvlt — TvltForPreTraining (TVLT model)
- unispeech — UniSpeechForPreTraining (UniSpeech model)
- unispeech-sat — UniSpeechSatForPreTraining (UniSpeechSat model)
- videomae — VideoMAEForPreTraining (VideoMAE model)
- vipllava — VipLlavaForConditionalGeneration (VipLlava model)
- visual_bert — VisualBertForPreTraining (VisualBERT model)
- vit_mae — ViTMAEForPreTraining (ViTMAE model)
- wav2vec2 — Wav2Vec2ForPreTraining (Wav2Vec2 model)
- wav2vec2-conformer — Wav2Vec2ConformerForPreTraining (Wav2Vec2-Conformer model)
- xlm — XLMWithLMHeadModel (XLM model)
- xlm-roberta — XLMRobertaForMaskedLM (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForMaskedLM (XLM-RoBERTa-XL model)
- xlnet — XLNetLMHeadModel (XLNet model)
- xmod — XmodForMaskedLM (X-MOD model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForPreTraining

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForPreTraining.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForPreTraining

###
class transformers.TFAutoModelForPreTraining

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: TFAlbertForPreTraining (ALBERT model)
  - BartConfig configuration class: TFBartForConditionalGeneration (BART model)
  - BertConfig configuration class: TFBertForPreTraining (BERT model)
  - CTRLConfig configuration class: TFCTRLLMHeadModel (CTRL model)
  - CamembertConfig configuration class: TFCamembertForMaskedLM (CamemBERT model)
  - DistilBertConfig configuration class: TFDistilBertForMaskedLM (DistilBERT model)
  - ElectraConfig configuration class: TFElectraForPreTraining (ELECTRA model)
  - FlaubertConfig configuration class: TFFlaubertWithLMHeadModel (FlauBERT model)
  - FunnelConfig configuration class: TFFunnelForPreTraining (Funnel Transformer model)
  - GPT2Config configuration class: TFGPT2LMHeadModel (OpenAI GPT-2 model)
  - LayoutLMConfig configuration class: TFLayoutLMForMaskedLM (LayoutLM model)
  - LxmertConfig configuration class: TFLxmertForPreTraining (LXMERT model)
  - MPNetConfig configuration class: TFMPNetForMaskedLM (MPNet model)
  - MobileBertConfig configuration class: TFMobileBertForPreTraining (MobileBERT model)
  - OpenAIGPTConfig configuration class: TFOpenAIGPTLMHeadModel (OpenAI GPT model)
  - RobertaConfig configuration class: TFRobertaForMaskedLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
  - T5Config configuration class: TFT5ForConditionalGeneration (T5 model)
  - TapasConfig configuration class: TFTapasForMaskedLM (TAPAS model)
  - TransfoXLConfig configuration class: TFTransfoXLLMHeadModel (Transformer-XL model)
  - ViTMAEConfig configuration class: TFViTMAEForPreTraining (ViTMAE model)
  - XLMConfig configuration class: TFXLMWithLMHeadModel (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaForMaskedLM (XLM-RoBERTa model)
  - XLNetConfig configuration class: TFXLNetLMHeadModel (XLNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a pretraining head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForPreTraining

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForPreTraining.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — TFAlbertForPreTraining (ALBERT model)
- bart — TFBartForConditionalGeneration (BART model)
- bert — TFBertForPreTraining (BERT model)
- camembert — TFCamembertForMaskedLM (CamemBERT model)
- ctrl — TFCTRLLMHeadModel (CTRL model)
- distilbert — TFDistilBertForMaskedLM (DistilBERT model)
- electra — TFElectraForPreTraining (ELECTRA model)
- flaubert — TFFlaubertWithLMHeadModel (FlauBERT model)
- funnel — TFFunnelForPreTraining (Funnel Transformer model)
- gpt-sw3 — TFGPT2LMHeadModel (GPT-Sw3 model)
- gpt2 — TFGPT2LMHeadModel (OpenAI GPT-2 model)
- layoutlm — TFLayoutLMForMaskedLM (LayoutLM model)
- lxmert — TFLxmertForPreTraining (LXMERT model)
- mobilebert — TFMobileBertForPreTraining (MobileBERT model)
- mpnet — TFMPNetForMaskedLM (MPNet model)
- openai-gpt — TFOpenAIGPTLMHeadModel (OpenAI GPT model)
- roberta — TFRobertaForMaskedLM (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
- t5 — TFT5ForConditionalGeneration (T5 model)
- tapas — TFTapasForMaskedLM (TAPAS model)
- transfo-xl — TFTransfoXLLMHeadModel (Transformer-XL model)
- vit_mae — TFViTMAEForPreTraining (ViTMAE model)
- xlm — TFXLMWithLMHeadModel (XLM model)
- xlm-roberta — TFXLMRobertaForMaskedLM (XLM-RoBERTa model)
- xlnet — TFXLNetLMHeadModel (XLNet model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForPreTraining

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForPreTraining.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForPreTraining

###
class transformers.FlaxAutoModelForPreTraining

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: FlaxAlbertForPreTraining (ALBERT model)
  - BartConfig configuration class: FlaxBartForConditionalGeneration (BART model)
  - BertConfig configuration class: FlaxBertForPreTraining (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdForPreTraining (BigBird model)
  - ElectraConfig configuration class: FlaxElectraForPreTraining (ELECTRA model)
  - LongT5Config configuration class: FlaxLongT5ForConditionalGeneration (LongT5 model)
  - MBartConfig configuration class: FlaxMBartForConditionalGeneration (mBART model)
  - MT5Config configuration class: FlaxMT5ForConditionalGeneration (MT5 model)
  - RoFormerConfig configuration class: FlaxRoFormerForMaskedLM (RoFormer model)
  - RobertaConfig configuration class: FlaxRobertaForMaskedLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
  - T5Config configuration class: FlaxT5ForConditionalGeneration (T5 model)
  - Wav2Vec2Config configuration class: FlaxWav2Vec2ForPreTraining (Wav2Vec2 model)
  - WhisperConfig configuration class: FlaxWhisperForConditionalGeneration (Whisper model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a pretraining head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForPreTraining

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForPreTraining.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — FlaxAlbertForPreTraining (ALBERT model)
- bart — FlaxBartForConditionalGeneration (BART model)
- bert — FlaxBertForPreTraining (BERT model)
- big_bird — FlaxBigBirdForPreTraining (BigBird model)
- electra — FlaxElectraForPreTraining (ELECTRA model)
- longt5 — FlaxLongT5ForConditionalGeneration (LongT5 model)
- mbart — FlaxMBartForConditionalGeneration (mBART model)
- mt5 — FlaxMT5ForConditionalGeneration (MT5 model)
- roberta — FlaxRobertaForMaskedLM (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
- roformer — FlaxRoFormerForMaskedLM (RoFormer model)
- t5 — FlaxT5ForConditionalGeneration (T5 model)
- wav2vec2 — FlaxWav2Vec2ForPreTraining (Wav2Vec2 model)
- whisper — FlaxWhisperForConditionalGeneration (Whisper model)
- xlm-roberta — FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForPreTraining

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForPreTraining.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForPreTraining.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

##
Natural Language Processing

The following auto classes are available for the following natural language processing tasks.

###
AutoModelForCausalLM

###
class transformers.AutoModelForCausalLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BartConfig configuration class: BartForCausalLM (BART model)
  - BertConfig configuration class: BertLMHeadModel (BERT model)
  - BertGenerationConfig configuration class: BertGenerationDecoder (Bert Generation model)
  - BigBirdConfig configuration class: BigBirdForCausalLM (BigBird model)
  - BigBirdPegasusConfig configuration class: BigBirdPegasusForCausalLM (BigBird-Pegasus model)
  - BioGptConfig configuration class: BioGptForCausalLM (BioGpt model)
  - BlenderbotConfig configuration class: BlenderbotForCausalLM (Blenderbot model)
  - BlenderbotSmallConfig configuration class: BlenderbotSmallForCausalLM (BlenderbotSmall model)
  - BloomConfig configuration class: BloomForCausalLM (BLOOM model)
  - CTRLConfig configuration class: CTRLLMHeadModel (CTRL model)
  - CamembertConfig configuration class: CamembertForCausalLM (CamemBERT model)
  - CodeGenConfig configuration class: CodeGenForCausalLM (CodeGen model)
  - CpmAntConfig configuration class: CpmAntForCausalLM (CPM-Ant model)
  - Data2VecTextConfig configuration class: Data2VecTextForCausalLM (Data2VecText model)
  - ElectraConfig configuration class: ElectraForCausalLM (ELECTRA model)
  - ErnieConfig configuration class: ErnieForCausalLM (ERNIE model)
  - FalconConfig configuration class: FalconForCausalLM (Falcon model)
  - FuyuConfig configuration class: FuyuForCausalLM (Fuyu model)
  - GPT2Config configuration class: GPT2LMHeadModel (OpenAI GPT-2 model)
  - GPTBigCodeConfig configuration class: GPTBigCodeForCausalLM (GPTBigCode model)
  - GPTJConfig configuration class: GPTJForCausalLM (GPT-J model)
  - GPTNeoConfig configuration class: GPTNeoForCausalLM (GPT Neo model)
  - GPTNeoXConfig configuration class: GPTNeoXForCausalLM (GPT NeoX model)
  - GPTNeoXJapaneseConfig configuration class: GPTNeoXJapaneseForCausalLM (GPT NeoX Japanese model)
  - GemmaConfig configuration class: GemmaForCausalLM (Gemma model)
  - GitConfig configuration class: GitForCausalLM (GIT model)
  - LlamaConfig configuration class: LlamaForCausalLM (LLaMA model)
  - MBartConfig configuration class: MBartForCausalLM (mBART model)
  - MambaConfig configuration class: MambaForCausalLM (Mamba model)
  - MarianConfig configuration class: MarianForCausalLM (Marian model)
  - MegaConfig configuration class: MegaForCausalLM (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertForCausalLM (Megatron-BERT model)
  - MistralConfig configuration class: MistralForCausalLM (Mistral model)
  - MixtralConfig configuration class: MixtralForCausalLM (Mixtral model)
  - MptConfig configuration class: MptForCausalLM (MPT model)
  - MusicgenConfig configuration class: MusicgenForCausalLM (MusicGen model)
  - MvpConfig configuration class: MvpForCausalLM (MVP model)
  - OPTConfig configuration class: OPTForCausalLM (OPT model)
  - OpenAIGPTConfig configuration class: OpenAIGPTLMHeadModel (OpenAI GPT model)
  - OpenLlamaConfig configuration class: OpenLlamaForCausalLM (OpenLlama model)
  - PLBartConfig configuration class: PLBartForCausalLM (PLBart model)
  - PegasusConfig configuration class: PegasusForCausalLM (Pegasus model)
  - PersimmonConfig configuration class: PersimmonForCausalLM (Persimmon model)
  - PhiConfig configuration class: PhiForCausalLM (Phi model)
  - ProphetNetConfig configuration class: ProphetNetForCausalLM (ProphetNet model)
  - QDQBertConfig configuration class: QDQBertLMHeadModel (QDQBert model)
  - Qwen2Config configuration class: Qwen2ForCausalLM (Qwen2 model)
  - ReformerConfig configuration class: ReformerModelWithLMHead (Reformer model)
  - RemBertConfig configuration class: RemBertForCausalLM (RemBERT model)
  - RoCBertConfig configuration class: RoCBertForCausalLM (RoCBert model)
  - RoFormerConfig configuration class: RoFormerForCausalLM (RoFormer model)
  - RobertaConfig configuration class: RobertaForCausalLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormForCausalLM (RoBERTa-PreLayerNorm model)
  - RwkvConfig configuration class: RwkvForCausalLM (RWKV model)
  - Speech2Text2Config configuration class: Speech2Text2ForCausalLM (Speech2Text2 model)
  - StableLmConfig configuration class: StableLmForCausalLM (StableLm model)
  - Starcoder2Config configuration class: Starcoder2ForCausalLM (Starcoder2 model)
  - TrOCRConfig configuration class: TrOCRForCausalLM (TrOCR model)
  - TransfoXLConfig configuration class: TransfoXLLMHeadModel (Transformer-XL model)
  - WhisperConfig configuration class: WhisperForCausalLM (Whisper model)
  - XGLMConfig configuration class: XGLMForCausalLM (XGLM model)
  - XLMConfig configuration class: XLMWithLMHeadModel (XLM model)
  - XLMProphetNetConfig configuration class: XLMProphetNetForCausalLM (XLM-ProphetNet model)
  - XLMRobertaConfig configuration class: XLMRobertaForCausalLM (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLForCausalLM (XLM-RoBERTa-XL model)
  - XLNetConfig configuration class: XLNetLMHeadModel (XLNet model)
  - XmodConfig configuration class: XmodForCausalLM (X-MOD model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForCausalLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForCausalLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bart — BartForCausalLM (BART model)
- bert — BertLMHeadModel (BERT model)
- bert-generation — BertGenerationDecoder (Bert Generation model)
- big_bird — BigBirdForCausalLM (BigBird model)
- bigbird_pegasus — BigBirdPegasusForCausalLM (BigBird-Pegasus model)
- biogpt — BioGptForCausalLM (BioGpt model)
- blenderbot — BlenderbotForCausalLM (Blenderbot model)
- blenderbot-small — BlenderbotSmallForCausalLM (BlenderbotSmall model)
- bloom — BloomForCausalLM (BLOOM model)
- camembert — CamembertForCausalLM (CamemBERT model)
- code_llama — LlamaForCausalLM (CodeLlama model)
- codegen — CodeGenForCausalLM (CodeGen model)
- cpmant — CpmAntForCausalLM (CPM-Ant model)
- ctrl — CTRLLMHeadModel (CTRL model)
- data2vec-text — Data2VecTextForCausalLM (Data2VecText model)
- electra — ElectraForCausalLM (ELECTRA model)
- ernie — ErnieForCausalLM (ERNIE model)
- falcon — FalconForCausalLM (Falcon model)
- fuyu — FuyuForCausalLM (Fuyu model)
- gemma — GemmaForCausalLM (Gemma model)
- git — GitForCausalLM (GIT model)
- gpt-sw3 — GPT2LMHeadModel (GPT-Sw3 model)
- gpt2 — GPT2LMHeadModel (OpenAI GPT-2 model)
- gpt_bigcode — GPTBigCodeForCausalLM (GPTBigCode model)
- gpt_neo — GPTNeoForCausalLM (GPT Neo model)
- gpt_neox — GPTNeoXForCausalLM (GPT NeoX model)
- gpt_neox_japanese — GPTNeoXJapaneseForCausalLM (GPT NeoX Japanese model)
- gptj — GPTJForCausalLM (GPT-J model)
- llama — LlamaForCausalLM (LLaMA model)
- mamba — MambaForCausalLM (Mamba model)
- marian — MarianForCausalLM (Marian model)
- mbart — MBartForCausalLM (mBART model)
- mega — MegaForCausalLM (MEGA model)
- megatron-bert — MegatronBertForCausalLM (Megatron-BERT model)
- mistral — MistralForCausalLM (Mistral model)
- mixtral — MixtralForCausalLM (Mixtral model)
- mpt — MptForCausalLM (MPT model)
- musicgen — MusicgenForCausalLM (MusicGen model)
- mvp — MvpForCausalLM (MVP model)
- open-llama — OpenLlamaForCausalLM (OpenLlama model)
- openai-gpt — OpenAIGPTLMHeadModel (OpenAI GPT model)
- opt — OPTForCausalLM (OPT model)
- pegasus — PegasusForCausalLM (Pegasus model)
- persimmon — PersimmonForCausalLM (Persimmon model)
- phi — PhiForCausalLM (Phi model)
- plbart — PLBartForCausalLM (PLBart model)
- prophetnet — ProphetNetForCausalLM (ProphetNet model)
- qdqbert — QDQBertLMHeadModel (QDQBert model)
- qwen2 — Qwen2ForCausalLM (Qwen2 model)
- reformer — ReformerModelWithLMHead (Reformer model)
- rembert — RemBertForCausalLM (RemBERT model)
- roberta — RobertaForCausalLM (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormForCausalLM (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertForCausalLM (RoCBert model)
- roformer — RoFormerForCausalLM (RoFormer model)
- rwkv — RwkvForCausalLM (RWKV model)
- speech_to_text_2 — Speech2Text2ForCausalLM (Speech2Text2 model)
- stablelm — StableLmForCausalLM (StableLm model)
- starcoder2 — Starcoder2ForCausalLM (Starcoder2 model)
- transfo-xl — TransfoXLLMHeadModel (Transformer-XL model)
- trocr — TrOCRForCausalLM (TrOCR model)
- whisper — WhisperForCausalLM (Whisper model)
- xglm — XGLMForCausalLM (XGLM model)
- xlm — XLMWithLMHeadModel (XLM model)
- xlm-prophetnet — XLMProphetNetForCausalLM (XLM-ProphetNet model)
- xlm-roberta — XLMRobertaForCausalLM (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForCausalLM (XLM-RoBERTa-XL model)
- xlnet — XLNetLMHeadModel (XLNet model)
- xmod — XmodForCausalLM (X-MOD model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForCausalLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForCausalLM.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForCausalLM

###
class transformers.TFAutoModelForCausalLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BertConfig configuration class: TFBertLMHeadModel (BERT model)
  - CTRLConfig configuration class: TFCTRLLMHeadModel (CTRL model)
  - CamembertConfig configuration class: TFCamembertForCausalLM (CamemBERT model)
  - GPT2Config configuration class: TFGPT2LMHeadModel (OpenAI GPT-2 model)
  - GPTJConfig configuration class: TFGPTJForCausalLM (GPT-J model)
  - OPTConfig configuration class: TFOPTForCausalLM (OPT model)
  - OpenAIGPTConfig configuration class: TFOpenAIGPTLMHeadModel (OpenAI GPT model)
  - RemBertConfig configuration class: TFRemBertForCausalLM (RemBERT model)
  - RoFormerConfig configuration class: TFRoFormerForCausalLM (RoFormer model)
  - RobertaConfig configuration class: TFRobertaForCausalLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForCausalLM (RoBERTa-PreLayerNorm model)
  - TransfoXLConfig configuration class: TFTransfoXLLMHeadModel (Transformer-XL model)
  - XGLMConfig configuration class: TFXGLMForCausalLM (XGLM model)
  - XLMConfig configuration class: TFXLMWithLMHeadModel (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaForCausalLM (XLM-RoBERTa model)
  - XLNetConfig configuration class: TFXLNetLMHeadModel (XLNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForCausalLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForCausalLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bert — TFBertLMHeadModel (BERT model)
- camembert — TFCamembertForCausalLM (CamemBERT model)
- ctrl — TFCTRLLMHeadModel (CTRL model)
- gpt-sw3 — TFGPT2LMHeadModel (GPT-Sw3 model)
- gpt2 — TFGPT2LMHeadModel (OpenAI GPT-2 model)
- gptj — TFGPTJForCausalLM (GPT-J model)
- openai-gpt — TFOpenAIGPTLMHeadModel (OpenAI GPT model)
- opt — TFOPTForCausalLM (OPT model)
- rembert — TFRemBertForCausalLM (RemBERT model)
- roberta — TFRobertaForCausalLM (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormForCausalLM (RoBERTa-PreLayerNorm model)
- roformer — TFRoFormerForCausalLM (RoFormer model)
- transfo-xl — TFTransfoXLLMHeadModel (Transformer-XL model)
- xglm — TFXGLMForCausalLM (XGLM model)
- xlm — TFXLMWithLMHeadModel (XLM model)
- xlm-roberta — TFXLMRobertaForCausalLM (XLM-RoBERTa model)
- xlnet — TFXLNetLMHeadModel (XLNet model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForCausalLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForCausalLM.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForCausalLM

###
class transformers.FlaxAutoModelForCausalLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BartConfig configuration class: FlaxBartForCausalLM (BART model)
  - BertConfig configuration class: FlaxBertForCausalLM (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdForCausalLM (BigBird model)
  - BloomConfig configuration class: FlaxBloomForCausalLM (BLOOM model)
  - ElectraConfig configuration class: FlaxElectraForCausalLM (ELECTRA model)
  - GPT2Config configuration class: FlaxGPT2LMHeadModel (OpenAI GPT-2 model)
  - GPTJConfig configuration class: FlaxGPTJForCausalLM (GPT-J model)
  - GPTNeoConfig configuration class: FlaxGPTNeoForCausalLM (GPT Neo model)
  - GemmaConfig configuration class: FlaxGemmaForCausalLM (Gemma model)
  - LlamaConfig configuration class: FlaxLlamaForCausalLM (LLaMA model)
  - MistralConfig configuration class: FlaxMistralForCausalLM (Mistral model)
  - OPTConfig configuration class: FlaxOPTForCausalLM (OPT model)
  - RobertaConfig configuration class: FlaxRobertaForCausalLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormForCausalLM (RoBERTa-PreLayerNorm model)
  - XGLMConfig configuration class: FlaxXGLMForCausalLM (XGLM model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaForCausalLM (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForCausalLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForCausalLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bart — FlaxBartForCausalLM (BART model)
- bert — FlaxBertForCausalLM (BERT model)
- big_bird — FlaxBigBirdForCausalLM (BigBird model)
- bloom — FlaxBloomForCausalLM (BLOOM model)
- electra — FlaxElectraForCausalLM (ELECTRA model)
- gemma — FlaxGemmaForCausalLM (Gemma model)
- gpt-sw3 — FlaxGPT2LMHeadModel (GPT-Sw3 model)
- gpt2 — FlaxGPT2LMHeadModel (OpenAI GPT-2 model)
- gpt_neo — FlaxGPTNeoForCausalLM (GPT Neo model)
- gptj — FlaxGPTJForCausalLM (GPT-J model)
- llama — FlaxLlamaForCausalLM (LLaMA model)
- mistral — FlaxMistralForCausalLM (Mistral model)
- opt — FlaxOPTForCausalLM (OPT model)
- roberta — FlaxRobertaForCausalLM (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormForCausalLM (RoBERTa-PreLayerNorm model)
- xglm — FlaxXGLMForCausalLM (XGLM model)
- xlm-roberta — FlaxXLMRobertaForCausalLM (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForCausalLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForCausalLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForCausalLM.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForMaskedLM

###
class transformers.AutoModelForMaskedLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: AlbertForMaskedLM (ALBERT model)
  - BartConfig configuration class: BartForConditionalGeneration (BART model)
  - BertConfig configuration class: BertForMaskedLM (BERT model)
  - BigBirdConfig configuration class: BigBirdForMaskedLM (BigBird model)
  - CamembertConfig configuration class: CamembertForMaskedLM (CamemBERT model)
  - ConvBertConfig configuration class: ConvBertForMaskedLM (ConvBERT model)
  - Data2VecTextConfig configuration class: Data2VecTextForMaskedLM (Data2VecText model)
  - DebertaConfig configuration class: DebertaForMaskedLM (DeBERTa model)
  - DebertaV2Config configuration class: DebertaV2ForMaskedLM (DeBERTa-v2 model)
  - DistilBertConfig configuration class: DistilBertForMaskedLM (DistilBERT model)
  - ElectraConfig configuration class: ElectraForMaskedLM (ELECTRA model)
  - ErnieConfig configuration class: ErnieForMaskedLM (ERNIE model)
  - EsmConfig configuration class: EsmForMaskedLM (ESM model)
  - FNetConfig configuration class: FNetForMaskedLM (FNet model)
  - FlaubertConfig configuration class: FlaubertWithLMHeadModel (FlauBERT model)
  - FunnelConfig configuration class: FunnelForMaskedLM (Funnel Transformer model)
  - IBertConfig configuration class: IBertForMaskedLM (I-BERT model)
  - LayoutLMConfig configuration class: LayoutLMForMaskedLM (LayoutLM model)
  - LongformerConfig configuration class: LongformerForMaskedLM (Longformer model)
  - LukeConfig configuration class: LukeForMaskedLM (LUKE model)
  - MBartConfig configuration class: MBartForConditionalGeneration (mBART model)
  - MPNetConfig configuration class: MPNetForMaskedLM (MPNet model)
  - MegaConfig configuration class: MegaForMaskedLM (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertForMaskedLM (Megatron-BERT model)
  - MobileBertConfig configuration class: MobileBertForMaskedLM (MobileBERT model)
  - MraConfig configuration class: MraForMaskedLM (MRA model)
  - MvpConfig configuration class: MvpForConditionalGeneration (MVP model)
  - NezhaConfig configuration class: NezhaForMaskedLM (Nezha model)
  - NystromformerConfig configuration class: NystromformerForMaskedLM (Nyströmformer model)
  - PerceiverConfig configuration class: PerceiverForMaskedLM (Perceiver model)
  - QDQBertConfig configuration class: QDQBertForMaskedLM (QDQBert model)
  - ReformerConfig configuration class: ReformerForMaskedLM (Reformer model)
  - RemBertConfig configuration class: RemBertForMaskedLM (RemBERT model)
  - RoCBertConfig configuration class: RoCBertForMaskedLM (RoCBert model)
  - RoFormerConfig configuration class: RoFormerForMaskedLM (RoFormer model)
  - RobertaConfig configuration class: RobertaForMaskedLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
  - SqueezeBertConfig configuration class: SqueezeBertForMaskedLM (SqueezeBERT model)
  - TapasConfig configuration class: TapasForMaskedLM (TAPAS model)
  - Wav2Vec2Config configuration class: `Wav2Vec2ForMaskedLM` (Wav2Vec2 model)
  - XLMConfig configuration class: XLMWithLMHeadModel (XLM model)
  - XLMRobertaConfig configuration class: XLMRobertaForMaskedLM (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLForMaskedLM (XLM-RoBERTa-XL model)
  - XmodConfig configuration class: XmodForMaskedLM (X-MOD model)
  - YosoConfig configuration class: YosoForMaskedLM (YOSO model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForMaskedLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForMaskedLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertForMaskedLM (ALBERT model)
- bart — BartForConditionalGeneration (BART model)
- bert — BertForMaskedLM (BERT model)
- big_bird — BigBirdForMaskedLM (BigBird model)
- camembert — CamembertForMaskedLM (CamemBERT model)
- convbert — ConvBertForMaskedLM (ConvBERT model)
- data2vec-text — Data2VecTextForMaskedLM (Data2VecText model)
- deberta — DebertaForMaskedLM (DeBERTa model)
- deberta-v2 — DebertaV2ForMaskedLM (DeBERTa-v2 model)
- distilbert — DistilBertForMaskedLM (DistilBERT model)
- electra — ElectraForMaskedLM (ELECTRA model)
- ernie — ErnieForMaskedLM (ERNIE model)
- esm — EsmForMaskedLM (ESM model)
- flaubert — FlaubertWithLMHeadModel (FlauBERT model)
- fnet — FNetForMaskedLM (FNet model)
- funnel — FunnelForMaskedLM (Funnel Transformer model)
- ibert — IBertForMaskedLM (I-BERT model)
- layoutlm — LayoutLMForMaskedLM (LayoutLM model)
- longformer — LongformerForMaskedLM (Longformer model)
- luke — LukeForMaskedLM (LUKE model)
- mbart — MBartForConditionalGeneration (mBART model)
- mega — MegaForMaskedLM (MEGA model)
- megatron-bert — MegatronBertForMaskedLM (Megatron-BERT model)
- mobilebert — MobileBertForMaskedLM (MobileBERT model)
- mpnet — MPNetForMaskedLM (MPNet model)
- mra — MraForMaskedLM (MRA model)
- mvp — MvpForConditionalGeneration (MVP model)
- nezha — NezhaForMaskedLM (Nezha model)
- nystromformer — NystromformerForMaskedLM (Nyströmformer model)
- perceiver — PerceiverForMaskedLM (Perceiver model)
- qdqbert — QDQBertForMaskedLM (QDQBert model)
- reformer — ReformerForMaskedLM (Reformer model)
- rembert — RemBertForMaskedLM (RemBERT model)
- roberta — RobertaForMaskedLM (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertForMaskedLM (RoCBert model)
- roformer — RoFormerForMaskedLM (RoFormer model)
- squeezebert — SqueezeBertForMaskedLM (SqueezeBERT model)
- tapas — TapasForMaskedLM (TAPAS model)
- wav2vec2 — `Wav2Vec2ForMaskedLM` (Wav2Vec2 model)
- xlm — XLMWithLMHeadModel (XLM model)
- xlm-roberta — XLMRobertaForMaskedLM (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForMaskedLM (XLM-RoBERTa-XL model)
- xmod — XmodForMaskedLM (X-MOD model)
- yoso — YosoForMaskedLM (YOSO model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForMaskedLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForMaskedLM.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForMaskedLM

###
class transformers.TFAutoModelForMaskedLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: TFAlbertForMaskedLM (ALBERT model)
  - BertConfig configuration class: TFBertForMaskedLM (BERT model)
  - CamembertConfig configuration class: TFCamembertForMaskedLM (CamemBERT model)
  - ConvBertConfig configuration class: TFConvBertForMaskedLM (ConvBERT model)
  - DebertaConfig configuration class: TFDebertaForMaskedLM (DeBERTa model)
  - DebertaV2Config configuration class: TFDebertaV2ForMaskedLM (DeBERTa-v2 model)
  - DistilBertConfig configuration class: TFDistilBertForMaskedLM (DistilBERT model)
  - ElectraConfig configuration class: TFElectraForMaskedLM (ELECTRA model)
  - EsmConfig configuration class: TFEsmForMaskedLM (ESM model)
  - FlaubertConfig configuration class: TFFlaubertWithLMHeadModel (FlauBERT model)
  - FunnelConfig configuration class: TFFunnelForMaskedLM (Funnel Transformer model)
  - LayoutLMConfig configuration class: TFLayoutLMForMaskedLM (LayoutLM model)
  - LongformerConfig configuration class: TFLongformerForMaskedLM (Longformer model)
  - MPNetConfig configuration class: TFMPNetForMaskedLM (MPNet model)
  - MobileBertConfig configuration class: TFMobileBertForMaskedLM (MobileBERT model)
  - RemBertConfig configuration class: TFRemBertForMaskedLM (RemBERT model)
  - RoFormerConfig configuration class: TFRoFormerForMaskedLM (RoFormer model)
  - RobertaConfig configuration class: TFRobertaForMaskedLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
  - TapasConfig configuration class: TFTapasForMaskedLM (TAPAS model)
  - XLMConfig configuration class: TFXLMWithLMHeadModel (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaForMaskedLM (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForMaskedLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForMaskedLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — TFAlbertForMaskedLM (ALBERT model)
- bert — TFBertForMaskedLM (BERT model)
- camembert — TFCamembertForMaskedLM (CamemBERT model)
- convbert — TFConvBertForMaskedLM (ConvBERT model)
- deberta — TFDebertaForMaskedLM (DeBERTa model)
- deberta-v2 — TFDebertaV2ForMaskedLM (DeBERTa-v2 model)
- distilbert — TFDistilBertForMaskedLM (DistilBERT model)
- electra — TFElectraForMaskedLM (ELECTRA model)
- esm — TFEsmForMaskedLM (ESM model)
- flaubert — TFFlaubertWithLMHeadModel (FlauBERT model)
- funnel — TFFunnelForMaskedLM (Funnel Transformer model)
- layoutlm — TFLayoutLMForMaskedLM (LayoutLM model)
- longformer — TFLongformerForMaskedLM (Longformer model)
- mobilebert — TFMobileBertForMaskedLM (MobileBERT model)
- mpnet — TFMPNetForMaskedLM (MPNet model)
- rembert — TFRemBertForMaskedLM (RemBERT model)
- roberta — TFRobertaForMaskedLM (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
- roformer — TFRoFormerForMaskedLM (RoFormer model)
- tapas — TFTapasForMaskedLM (TAPAS model)
- xlm — TFXLMWithLMHeadModel (XLM model)
- xlm-roberta — TFXLMRobertaForMaskedLM (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForMaskedLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForMaskedLM.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForMaskedLM

###
class transformers.FlaxAutoModelForMaskedLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: FlaxAlbertForMaskedLM (ALBERT model)
  - BartConfig configuration class: FlaxBartForConditionalGeneration (BART model)
  - BertConfig configuration class: FlaxBertForMaskedLM (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdForMaskedLM (BigBird model)
  - DistilBertConfig configuration class: FlaxDistilBertForMaskedLM (DistilBERT model)
  - ElectraConfig configuration class: FlaxElectraForMaskedLM (ELECTRA model)
  - MBartConfig configuration class: FlaxMBartForConditionalGeneration (mBART model)
  - RoFormerConfig configuration class: FlaxRoFormerForMaskedLM (RoFormer model)
  - RobertaConfig configuration class: FlaxRobertaForMaskedLM (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForMaskedLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForMaskedLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — FlaxAlbertForMaskedLM (ALBERT model)
- bart — FlaxBartForConditionalGeneration (BART model)
- bert — FlaxBertForMaskedLM (BERT model)
- big_bird — FlaxBigBirdForMaskedLM (BigBird model)
- distilbert — FlaxDistilBertForMaskedLM (DistilBERT model)
- electra — FlaxElectraForMaskedLM (ELECTRA model)
- mbart — FlaxMBartForConditionalGeneration (mBART model)
- roberta — FlaxRobertaForMaskedLM (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormForMaskedLM (RoBERTa-PreLayerNorm model)
- roformer — FlaxRoFormerForMaskedLM (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForMaskedLM (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForMaskedLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForMaskedLM.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForMaskGeneration

###
class transformers.AutoModelForMaskGeneration

< source >

( *args **kwargs )

###
TFAutoModelForMaskGeneration

###
class transformers.TFAutoModelForMaskGeneration

< source >

( *args **kwargs )

###
AutoModelForSeq2SeqLM

###
class transformers.AutoModelForSeq2SeqLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BartConfig configuration class: BartForConditionalGeneration (BART model)
  - BigBirdPegasusConfig configuration class: BigBirdPegasusForConditionalGeneration (BigBird-Pegasus model)
  - BlenderbotConfig configuration class: BlenderbotForConditionalGeneration (Blenderbot model)
  - BlenderbotSmallConfig configuration class: BlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
  - EncoderDecoderConfig configuration class: EncoderDecoderModel (Encoder decoder model)
  - FSMTConfig configuration class: FSMTForConditionalGeneration (FairSeq Machine-Translation model)
  - GPTSanJapaneseConfig configuration class: GPTSanJapaneseForConditionalGeneration (GPTSAN-japanese model)
  - LEDConfig configuration class: LEDForConditionalGeneration (LED model)
  - LongT5Config configuration class: LongT5ForConditionalGeneration (LongT5 model)
  - M2M100Config configuration class: M2M100ForConditionalGeneration (M2M100 model)
  - MBartConfig configuration class: MBartForConditionalGeneration (mBART model)
  - MT5Config configuration class: MT5ForConditionalGeneration (MT5 model)
  - MarianConfig configuration class: MarianMTModel (Marian model)
  - MvpConfig configuration class: MvpForConditionalGeneration (MVP model)
  - NllbMoeConfig configuration class: NllbMoeForConditionalGeneration (NLLB-MOE model)
  - PLBartConfig configuration class: PLBartForConditionalGeneration (PLBart model)
  - PegasusConfig configuration class: PegasusForConditionalGeneration (Pegasus model)
  - PegasusXConfig configuration class: PegasusXForConditionalGeneration (PEGASUS-X model)
  - ProphetNetConfig configuration class: ProphetNetForConditionalGeneration (ProphetNet model)
  - SeamlessM4TConfig configuration class: SeamlessM4TForTextToText (SeamlessM4T model)
  - SeamlessM4Tv2Config configuration class: SeamlessM4Tv2ForTextToText (SeamlessM4Tv2 model)
  - SwitchTransformersConfig configuration class: SwitchTransformersForConditionalGeneration (SwitchTransformers model)
  - T5Config configuration class: T5ForConditionalGeneration (T5 model)
  - UMT5Config configuration class: UMT5ForConditionalGeneration (UMT5 model)
  - XLMProphetNetConfig configuration class: XLMProphetNetForConditionalGeneration (XLM-ProphetNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForSeq2SeqLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-t5/t5-base")
    >>> model = AutoModelForSeq2SeqLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bart — BartForConditionalGeneration (BART model)
- bigbird_pegasus — BigBirdPegasusForConditionalGeneration (BigBird-Pegasus model)
- blenderbot — BlenderbotForConditionalGeneration (Blenderbot model)
- blenderbot-small — BlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- encoder-decoder — EncoderDecoderModel (Encoder decoder model)
- fsmt — FSMTForConditionalGeneration (FairSeq Machine-Translation model)
- gptsan-japanese — GPTSanJapaneseForConditionalGeneration (GPTSAN-japanese model)
- led — LEDForConditionalGeneration (LED model)
- longt5 — LongT5ForConditionalGeneration (LongT5 model)
- m2m_100 — M2M100ForConditionalGeneration (M2M100 model)
- marian — MarianMTModel (Marian model)
- mbart — MBartForConditionalGeneration (mBART model)
- mt5 — MT5ForConditionalGeneration (MT5 model)
- mvp — MvpForConditionalGeneration (MVP model)
- nllb-moe — NllbMoeForConditionalGeneration (NLLB-MOE model)
- pegasus — PegasusForConditionalGeneration (Pegasus model)
- pegasus_x — PegasusXForConditionalGeneration (PEGASUS-X model)
- plbart — PLBartForConditionalGeneration (PLBart model)
- prophetnet — ProphetNetForConditionalGeneration (ProphetNet model)
- seamless_m4t — SeamlessM4TForTextToText (SeamlessM4T model)
- seamless_m4t_v2 — SeamlessM4Tv2ForTextToText (SeamlessM4Tv2 model)
- switch_transformers — SwitchTransformersForConditionalGeneration (SwitchTransformers model)
- t5 — T5ForConditionalGeneration (T5 model)
- umt5 — UMT5ForConditionalGeneration (UMT5 model)
- xlm-prophetnet — XLMProphetNetForConditionalGeneration (XLM-ProphetNet model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForSeq2SeqLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")

    >>> # Update configuration during loading
    >>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/t5_tf_model_config.json")
    >>> model = AutoModelForSeq2SeqLM.from_pretrained(
    ...     "./tf_model/t5_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForSeq2SeqLM

###
class transformers.TFAutoModelForSeq2SeqLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BartConfig configuration class: TFBartForConditionalGeneration (BART model)
  - BlenderbotConfig configuration class: TFBlenderbotForConditionalGeneration (Blenderbot model)
  - BlenderbotSmallConfig configuration class: TFBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
  - EncoderDecoderConfig configuration class: TFEncoderDecoderModel (Encoder decoder model)
  - LEDConfig configuration class: TFLEDForConditionalGeneration (LED model)
  - MBartConfig configuration class: TFMBartForConditionalGeneration (mBART model)
  - MT5Config configuration class: TFMT5ForConditionalGeneration (MT5 model)
  - MarianConfig configuration class: TFMarianMTModel (Marian model)
  - PegasusConfig configuration class: TFPegasusForConditionalGeneration (Pegasus model)
  - T5Config configuration class: TFT5ForConditionalGeneration (T5 model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-t5/t5-base")
    >>> model = TFAutoModelForSeq2SeqLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bart — TFBartForConditionalGeneration (BART model)
- blenderbot — TFBlenderbotForConditionalGeneration (Blenderbot model)
- blenderbot-small — TFBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- encoder-decoder — TFEncoderDecoderModel (Encoder decoder model)
- led — TFLEDForConditionalGeneration (LED model)
- marian — TFMarianMTModel (Marian model)
- mbart — TFMBartForConditionalGeneration (mBART model)
- mt5 — TFMT5ForConditionalGeneration (MT5 model)
- pegasus — TFPegasusForConditionalGeneration (Pegasus model)
- t5 — TFT5ForConditionalGeneration (T5 model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSeq2SeqLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
    >>> model = TFAutoModelForSeq2SeqLM.from_pretrained(
    ...     "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForSeq2SeqLM

###
class transformers.FlaxAutoModelForSeq2SeqLM

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BartConfig configuration class: FlaxBartForConditionalGeneration (BART model)
  - BlenderbotConfig configuration class: FlaxBlenderbotForConditionalGeneration (Blenderbot model)
  - BlenderbotSmallConfig configuration class: FlaxBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
  - EncoderDecoderConfig configuration class: FlaxEncoderDecoderModel (Encoder decoder model)
  - LongT5Config configuration class: FlaxLongT5ForConditionalGeneration (LongT5 model)
  - MBartConfig configuration class: FlaxMBartForConditionalGeneration (mBART model)
  - MT5Config configuration class: FlaxMT5ForConditionalGeneration (MT5 model)
  - MarianConfig configuration class: FlaxMarianMTModel (Marian model)
  - PegasusConfig configuration class: FlaxPegasusForConditionalGeneration (Pegasus model)
  - T5Config configuration class: FlaxT5ForConditionalGeneration (T5 model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-t5/t5-base")
    >>> model = FlaxAutoModelForSeq2SeqLM.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bart — FlaxBartForConditionalGeneration (BART model)
- blenderbot — FlaxBlenderbotForConditionalGeneration (Blenderbot model)
- blenderbot-small — FlaxBlenderbotSmallForConditionalGeneration (BlenderbotSmall model)
- encoder-decoder — FlaxEncoderDecoderModel (Encoder decoder model)
- longt5 — FlaxLongT5ForConditionalGeneration (LongT5 model)
- marian — FlaxMarianMTModel (Marian model)
- mbart — FlaxMBartForConditionalGeneration (mBART model)
- mt5 — FlaxMT5ForConditionalGeneration (MT5 model)
- pegasus — FlaxPegasusForConditionalGeneration (Pegasus model)
- t5 — FlaxT5ForConditionalGeneration (T5 model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/t5_pt_model_config.json")
    >>> model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
    ...     "./pt_model/t5_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForSequenceClassification

###
class transformers.AutoModelForSequenceClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: AlbertForSequenceClassification (ALBERT model)
  - BartConfig configuration class: BartForSequenceClassification (BART model)
  - BertConfig configuration class: BertForSequenceClassification (BERT model)
  - BigBirdConfig configuration class: BigBirdForSequenceClassification (BigBird model)
  - BigBirdPegasusConfig configuration class: BigBirdPegasusForSequenceClassification (BigBird-Pegasus model)
  - BioGptConfig configuration class: BioGptForSequenceClassification (BioGpt model)
  - BloomConfig configuration class: BloomForSequenceClassification (BLOOM model)
  - CTRLConfig configuration class: CTRLForSequenceClassification (CTRL model)
  - CamembertConfig configuration class: CamembertForSequenceClassification (CamemBERT model)
  - CanineConfig configuration class: CanineForSequenceClassification (CANINE model)
  - ConvBertConfig configuration class: ConvBertForSequenceClassification (ConvBERT model)
  - Data2VecTextConfig configuration class: Data2VecTextForSequenceClassification (Data2VecText model)
  - DebertaConfig configuration class: DebertaForSequenceClassification (DeBERTa model)
  - DebertaV2Config configuration class: DebertaV2ForSequenceClassification (DeBERTa-v2 model)
  - DistilBertConfig configuration class: DistilBertForSequenceClassification (DistilBERT model)
  - ElectraConfig configuration class: ElectraForSequenceClassification (ELECTRA model)
  - ErnieConfig configuration class: ErnieForSequenceClassification (ERNIE model)
  - ErnieMConfig configuration class: ErnieMForSequenceClassification (ErnieM model)
  - EsmConfig configuration class: EsmForSequenceClassification (ESM model)
  - FNetConfig configuration class: FNetForSequenceClassification (FNet model)
  - FalconConfig configuration class: FalconForSequenceClassification (Falcon model)
  - FlaubertConfig configuration class: FlaubertForSequenceClassification (FlauBERT model)
  - FunnelConfig configuration class: FunnelForSequenceClassification (Funnel Transformer model)
  - GPT2Config configuration class: GPT2ForSequenceClassification (OpenAI GPT-2 model)
  - GPTBigCodeConfig configuration class: GPTBigCodeForSequenceClassification (GPTBigCode model)
  - GPTJConfig configuration class: GPTJForSequenceClassification (GPT-J model)
  - GPTNeoConfig configuration class: GPTNeoForSequenceClassification (GPT Neo model)
  - GPTNeoXConfig configuration class: GPTNeoXForSequenceClassification (GPT NeoX model)
  - GemmaConfig configuration class: GemmaForSequenceClassification (Gemma model)
  - IBertConfig configuration class: IBertForSequenceClassification (I-BERT model)
  - LEDConfig configuration class: LEDForSequenceClassification (LED model)
  - LayoutLMConfig configuration class: LayoutLMForSequenceClassification (LayoutLM model)
  - LayoutLMv2Config configuration class: LayoutLMv2ForSequenceClassification (LayoutLMv2 model)
  - LayoutLMv3Config configuration class: LayoutLMv3ForSequenceClassification (LayoutLMv3 model)
  - LiltConfig configuration class: LiltForSequenceClassification (LiLT model)
  - LlamaConfig configuration class: LlamaForSequenceClassification (LLaMA model)
  - LongformerConfig configuration class: LongformerForSequenceClassification (Longformer model)
  - LukeConfig configuration class: LukeForSequenceClassification (LUKE model)
  - MBartConfig configuration class: MBartForSequenceClassification (mBART model)
  - MPNetConfig configuration class: MPNetForSequenceClassification (MPNet model)
  - MT5Config configuration class: MT5ForSequenceClassification (MT5 model)
  - MarkupLMConfig configuration class: MarkupLMForSequenceClassification (MarkupLM model)
  - MegaConfig configuration class: MegaForSequenceClassification (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertForSequenceClassification (Megatron-BERT model)
  - MistralConfig configuration class: MistralForSequenceClassification (Mistral model)
  - MixtralConfig configuration class: MixtralForSequenceClassification (Mixtral model)
  - MobileBertConfig configuration class: MobileBertForSequenceClassification (MobileBERT model)
  - MptConfig configuration class: MptForSequenceClassification (MPT model)
  - MraConfig configuration class: MraForSequenceClassification (MRA model)
  - MvpConfig configuration class: MvpForSequenceClassification (MVP model)
  - NezhaConfig configuration class: NezhaForSequenceClassification (Nezha model)
  - NystromformerConfig configuration class: NystromformerForSequenceClassification (Nyströmformer model)
  - OPTConfig configuration class: OPTForSequenceClassification (OPT model)
  - OpenAIGPTConfig configuration class: OpenAIGPTForSequenceClassification (OpenAI GPT model)
  - OpenLlamaConfig configuration class: OpenLlamaForSequenceClassification (OpenLlama model)
  - PLBartConfig configuration class: PLBartForSequenceClassification (PLBart model)
  - PerceiverConfig configuration class: PerceiverForSequenceClassification (Perceiver model)
  - PersimmonConfig configuration class: PersimmonForSequenceClassification (Persimmon model)
  - PhiConfig configuration class: PhiForSequenceClassification (Phi model)
  - QDQBertConfig configuration class: QDQBertForSequenceClassification (QDQBert model)
  - Qwen2Config configuration class: Qwen2ForSequenceClassification (Qwen2 model)
  - ReformerConfig configuration class: ReformerForSequenceClassification (Reformer model)
  - RemBertConfig configuration class: RemBertForSequenceClassification (RemBERT model)
  - RoCBertConfig configuration class: RoCBertForSequenceClassification (RoCBert model)
  - RoFormerConfig configuration class: RoFormerForSequenceClassification (RoFormer model)
  - RobertaConfig configuration class: RobertaForSequenceClassification (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormForSequenceClassification (RoBERTa-PreLayerNorm model)
  - SqueezeBertConfig configuration class: SqueezeBertForSequenceClassification (SqueezeBERT model)
  - StableLmConfig configuration class: StableLmForSequenceClassification (StableLm model)
  - Starcoder2Config configuration class: Starcoder2ForSequenceClassification (Starcoder2 model)
  - T5Config configuration class: T5ForSequenceClassification (T5 model)
  - TapasConfig configuration class: TapasForSequenceClassification (TAPAS model)
  - TransfoXLConfig configuration class: TransfoXLForSequenceClassification (Transformer-XL model)
  - UMT5Config configuration class: UMT5ForSequenceClassification (UMT5 model)
  - XLMConfig configuration class: XLMForSequenceClassification (XLM model)
  - XLMRobertaConfig configuration class: XLMRobertaForSequenceClassification (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLForSequenceClassification (XLM-RoBERTa-XL model)
  - XLNetConfig configuration class: XLNetForSequenceClassification (XLNet model)
  - XmodConfig configuration class: XmodForSequenceClassification (X-MOD model)
  - YosoConfig configuration class: YosoForSequenceClassification (YOSO model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForSequenceClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForSequenceClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertForSequenceClassification (ALBERT model)
- bart — BartForSequenceClassification (BART model)
- bert — BertForSequenceClassification (BERT model)
- big_bird — BigBirdForSequenceClassification (BigBird model)
- bigbird_pegasus — BigBirdPegasusForSequenceClassification (BigBird-Pegasus model)
- biogpt — BioGptForSequenceClassification (BioGpt model)
- bloom — BloomForSequenceClassification (BLOOM model)
- camembert — CamembertForSequenceClassification (CamemBERT model)
- canine — CanineForSequenceClassification (CANINE model)
- code_llama — LlamaForSequenceClassification (CodeLlama model)
- convbert — ConvBertForSequenceClassification (ConvBERT model)
- ctrl — CTRLForSequenceClassification (CTRL model)
- data2vec-text — Data2VecTextForSequenceClassification (Data2VecText model)
- deberta — DebertaForSequenceClassification (DeBERTa model)
- deberta-v2 — DebertaV2ForSequenceClassification (DeBERTa-v2 model)
- distilbert — DistilBertForSequenceClassification (DistilBERT model)
- electra — ElectraForSequenceClassification (ELECTRA model)
- ernie — ErnieForSequenceClassification (ERNIE model)
- ernie_m — ErnieMForSequenceClassification (ErnieM model)
- esm — EsmForSequenceClassification (ESM model)
- falcon — FalconForSequenceClassification (Falcon model)
- flaubert — FlaubertForSequenceClassification (FlauBERT model)
- fnet — FNetForSequenceClassification (FNet model)
- funnel — FunnelForSequenceClassification (Funnel Transformer model)
- gemma — GemmaForSequenceClassification (Gemma model)
- gpt-sw3 — GPT2ForSequenceClassification (GPT-Sw3 model)
- gpt2 — GPT2ForSequenceClassification (OpenAI GPT-2 model)
- gpt_bigcode — GPTBigCodeForSequenceClassification (GPTBigCode model)
- gpt_neo — GPTNeoForSequenceClassification (GPT Neo model)
- gpt_neox — GPTNeoXForSequenceClassification (GPT NeoX model)
- gptj — GPTJForSequenceClassification (GPT-J model)
- ibert — IBertForSequenceClassification (I-BERT model)
- layoutlm — LayoutLMForSequenceClassification (LayoutLM model)
- layoutlmv2 — LayoutLMv2ForSequenceClassification (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ForSequenceClassification (LayoutLMv3 model)
- led — LEDForSequenceClassification (LED model)
- lilt — LiltForSequenceClassification (LiLT model)
- llama — LlamaForSequenceClassification (LLaMA model)
- longformer — LongformerForSequenceClassification (Longformer model)
- luke — LukeForSequenceClassification (LUKE model)
- markuplm — MarkupLMForSequenceClassification (MarkupLM model)
- mbart — MBartForSequenceClassification (mBART model)
- mega — MegaForSequenceClassification (MEGA model)
- megatron-bert — MegatronBertForSequenceClassification (Megatron-BERT model)
- mistral — MistralForSequenceClassification (Mistral model)
- mixtral — MixtralForSequenceClassification (Mixtral model)
- mobilebert — MobileBertForSequenceClassification (MobileBERT model)
- mpnet — MPNetForSequenceClassification (MPNet model)
- mpt — MptForSequenceClassification (MPT model)
- mra — MraForSequenceClassification (MRA model)
- mt5 — MT5ForSequenceClassification (MT5 model)
- mvp — MvpForSequenceClassification (MVP model)
- nezha — NezhaForSequenceClassification (Nezha model)
- nystromformer — NystromformerForSequenceClassification (Nyströmformer model)
- open-llama — OpenLlamaForSequenceClassification (OpenLlama model)
- openai-gpt — OpenAIGPTForSequenceClassification (OpenAI GPT model)
- opt — OPTForSequenceClassification (OPT model)
- perceiver — PerceiverForSequenceClassification (Perceiver model)
- persimmon — PersimmonForSequenceClassification (Persimmon model)
- phi — PhiForSequenceClassification (Phi model)
- plbart — PLBartForSequenceClassification (PLBart model)
- qdqbert — QDQBertForSequenceClassification (QDQBert model)
- qwen2 — Qwen2ForSequenceClassification (Qwen2 model)
- reformer — ReformerForSequenceClassification (Reformer model)
- rembert — RemBertForSequenceClassification (RemBERT model)
- roberta — RobertaForSequenceClassification (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormForSequenceClassification (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertForSequenceClassification (RoCBert model)
- roformer — RoFormerForSequenceClassification (RoFormer model)
- squeezebert — SqueezeBertForSequenceClassification (SqueezeBERT model)
- stablelm — StableLmForSequenceClassification (StableLm model)
- starcoder2 — Starcoder2ForSequenceClassification (Starcoder2 model)
- t5 — T5ForSequenceClassification (T5 model)
- tapas — TapasForSequenceClassification (TAPAS model)
- transfo-xl — TransfoXLForSequenceClassification (Transformer-XL model)
- umt5 — UMT5ForSequenceClassification (UMT5 model)
- xlm — XLMForSequenceClassification (XLM model)
- xlm-roberta — XLMRobertaForSequenceClassification (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForSequenceClassification (XLM-RoBERTa-XL model)
- xlnet — XLNetForSequenceClassification (XLNet model)
- xmod — XmodForSequenceClassification (X-MOD model)
- yoso — YosoForSequenceClassification (YOSO model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForSequenceClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForSequenceClassification.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForSequenceClassification

###
class transformers.TFAutoModelForSequenceClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: TFAlbertForSequenceClassification (ALBERT model)
  - BartConfig configuration class: TFBartForSequenceClassification (BART model)
  - BertConfig configuration class: TFBertForSequenceClassification (BERT model)
  - CTRLConfig configuration class: TFCTRLForSequenceClassification (CTRL model)
  - CamembertConfig configuration class: TFCamembertForSequenceClassification (CamemBERT model)
  - ConvBertConfig configuration class: TFConvBertForSequenceClassification (ConvBERT model)
  - DebertaConfig configuration class: TFDebertaForSequenceClassification (DeBERTa model)
  - DebertaV2Config configuration class: TFDebertaV2ForSequenceClassification (DeBERTa-v2 model)
  - DistilBertConfig configuration class: TFDistilBertForSequenceClassification (DistilBERT model)
  - ElectraConfig configuration class: TFElectraForSequenceClassification (ELECTRA model)
  - EsmConfig configuration class: TFEsmForSequenceClassification (ESM model)
  - FlaubertConfig configuration class: TFFlaubertForSequenceClassification (FlauBERT model)
  - FunnelConfig configuration class: TFFunnelForSequenceClassification (Funnel Transformer model)
  - GPT2Config configuration class: TFGPT2ForSequenceClassification (OpenAI GPT-2 model)
  - GPTJConfig configuration class: TFGPTJForSequenceClassification (GPT-J model)
  - LayoutLMConfig configuration class: TFLayoutLMForSequenceClassification (LayoutLM model)
  - LayoutLMv3Config configuration class: TFLayoutLMv3ForSequenceClassification (LayoutLMv3 model)
  - LongformerConfig configuration class: TFLongformerForSequenceClassification (Longformer model)
  - MPNetConfig configuration class: TFMPNetForSequenceClassification (MPNet model)
  - MobileBertConfig configuration class: TFMobileBertForSequenceClassification (MobileBERT model)
  - OpenAIGPTConfig configuration class: TFOpenAIGPTForSequenceClassification (OpenAI GPT model)
  - RemBertConfig configuration class: TFRemBertForSequenceClassification (RemBERT model)
  - RoFormerConfig configuration class: TFRoFormerForSequenceClassification (RoFormer model)
  - RobertaConfig configuration class: TFRobertaForSequenceClassification (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForSequenceClassification (RoBERTa-PreLayerNorm model)
  - TapasConfig configuration class: TFTapasForSequenceClassification (TAPAS model)
  - TransfoXLConfig configuration class: TFTransfoXLForSequenceClassification (Transformer-XL model)
  - XLMConfig configuration class: TFXLMForSequenceClassification (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaForSequenceClassification (XLM-RoBERTa model)
  - XLNetConfig configuration class: TFXLNetForSequenceClassification (XLNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSequenceClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForSequenceClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — TFAlbertForSequenceClassification (ALBERT model)
- bart — TFBartForSequenceClassification (BART model)
- bert — TFBertForSequenceClassification (BERT model)
- camembert — TFCamembertForSequenceClassification (CamemBERT model)
- convbert — TFConvBertForSequenceClassification (ConvBERT model)
- ctrl — TFCTRLForSequenceClassification (CTRL model)
- deberta — TFDebertaForSequenceClassification (DeBERTa model)
- deberta-v2 — TFDebertaV2ForSequenceClassification (DeBERTa-v2 model)
- distilbert — TFDistilBertForSequenceClassification (DistilBERT model)
- electra — TFElectraForSequenceClassification (ELECTRA model)
- esm — TFEsmForSequenceClassification (ESM model)
- flaubert — TFFlaubertForSequenceClassification (FlauBERT model)
- funnel — TFFunnelForSequenceClassification (Funnel Transformer model)
- gpt-sw3 — TFGPT2ForSequenceClassification (GPT-Sw3 model)
- gpt2 — TFGPT2ForSequenceClassification (OpenAI GPT-2 model)
- gptj — TFGPTJForSequenceClassification (GPT-J model)
- layoutlm — TFLayoutLMForSequenceClassification (LayoutLM model)
- layoutlmv3 — TFLayoutLMv3ForSequenceClassification (LayoutLMv3 model)
- longformer — TFLongformerForSequenceClassification (Longformer model)
- mobilebert — TFMobileBertForSequenceClassification (MobileBERT model)
- mpnet — TFMPNetForSequenceClassification (MPNet model)
- openai-gpt — TFOpenAIGPTForSequenceClassification (OpenAI GPT model)
- rembert — TFRemBertForSequenceClassification (RemBERT model)
- roberta — TFRobertaForSequenceClassification (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormForSequenceClassification (RoBERTa-PreLayerNorm model)
- roformer — TFRoFormerForSequenceClassification (RoFormer model)
- tapas — TFTapasForSequenceClassification (TAPAS model)
- transfo-xl — TFTransfoXLForSequenceClassification (Transformer-XL model)
- xlm — TFXLMForSequenceClassification (XLM model)
- xlm-roberta — TFXLMRobertaForSequenceClassification (XLM-RoBERTa model)
- xlnet — TFXLNetForSequenceClassification (XLNet model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSequenceClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForSequenceClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForSequenceClassification

###
class transformers.FlaxAutoModelForSequenceClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: FlaxAlbertForSequenceClassification (ALBERT model)
  - BartConfig configuration class: FlaxBartForSequenceClassification (BART model)
  - BertConfig configuration class: FlaxBertForSequenceClassification (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdForSequenceClassification (BigBird model)
  - DistilBertConfig configuration class: FlaxDistilBertForSequenceClassification (DistilBERT model)
  - ElectraConfig configuration class: FlaxElectraForSequenceClassification (ELECTRA model)
  - MBartConfig configuration class: FlaxMBartForSequenceClassification (mBART model)
  - RoFormerConfig configuration class: FlaxRoFormerForSequenceClassification (RoFormer model)
  - RobertaConfig configuration class: FlaxRobertaForSequenceClassification (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormForSequenceClassification (RoBERTa-PreLayerNorm model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaForSequenceClassification (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForSequenceClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — FlaxAlbertForSequenceClassification (ALBERT model)
- bart — FlaxBartForSequenceClassification (BART model)
- bert — FlaxBertForSequenceClassification (BERT model)
- big_bird — FlaxBigBirdForSequenceClassification (BigBird model)
- distilbert — FlaxDistilBertForSequenceClassification (DistilBERT model)
- electra — FlaxElectraForSequenceClassification (ELECTRA model)
- mbart — FlaxMBartForSequenceClassification (mBART model)
- roberta — FlaxRobertaForSequenceClassification (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormForSequenceClassification (RoBERTa-PreLayerNorm model)
- roformer — FlaxRoFormerForSequenceClassification (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForSequenceClassification (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForSequenceClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForSequenceClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForSequenceClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForMultipleChoice

###
class transformers.AutoModelForMultipleChoice

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: AlbertForMultipleChoice (ALBERT model)
  - BertConfig configuration class: BertForMultipleChoice (BERT model)
  - BigBirdConfig configuration class: BigBirdForMultipleChoice (BigBird model)
  - CamembertConfig configuration class: CamembertForMultipleChoice (CamemBERT model)
  - CanineConfig configuration class: CanineForMultipleChoice (CANINE model)
  - ConvBertConfig configuration class: ConvBertForMultipleChoice (ConvBERT model)
  - Data2VecTextConfig configuration class: Data2VecTextForMultipleChoice (Data2VecText model)
  - DebertaV2Config configuration class: DebertaV2ForMultipleChoice (DeBERTa-v2 model)
  - DistilBertConfig configuration class: DistilBertForMultipleChoice (DistilBERT model)
  - ElectraConfig configuration class: ElectraForMultipleChoice (ELECTRA model)
  - ErnieConfig configuration class: ErnieForMultipleChoice (ERNIE model)
  - ErnieMConfig configuration class: ErnieMForMultipleChoice (ErnieM model)
  - FNetConfig configuration class: FNetForMultipleChoice (FNet model)
  - FlaubertConfig configuration class: FlaubertForMultipleChoice (FlauBERT model)
  - FunnelConfig configuration class: FunnelForMultipleChoice (Funnel Transformer model)
  - IBertConfig configuration class: IBertForMultipleChoice (I-BERT model)
  - LongformerConfig configuration class: LongformerForMultipleChoice (Longformer model)
  - LukeConfig configuration class: LukeForMultipleChoice (LUKE model)
  - MPNetConfig configuration class: MPNetForMultipleChoice (MPNet model)
  - MegaConfig configuration class: MegaForMultipleChoice (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertForMultipleChoice (Megatron-BERT model)
  - MobileBertConfig configuration class: MobileBertForMultipleChoice (MobileBERT model)
  - MraConfig configuration class: MraForMultipleChoice (MRA model)
  - NezhaConfig configuration class: NezhaForMultipleChoice (Nezha model)
  - NystromformerConfig configuration class: NystromformerForMultipleChoice (Nyströmformer model)
  - QDQBertConfig configuration class: QDQBertForMultipleChoice (QDQBert model)
  - RemBertConfig configuration class: RemBertForMultipleChoice (RemBERT model)
  - RoCBertConfig configuration class: RoCBertForMultipleChoice (RoCBert model)
  - RoFormerConfig configuration class: RoFormerForMultipleChoice (RoFormer model)
  - RobertaConfig configuration class: RobertaForMultipleChoice (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormForMultipleChoice (RoBERTa-PreLayerNorm model)
  - SqueezeBertConfig configuration class: SqueezeBertForMultipleChoice (SqueezeBERT model)
  - XLMConfig configuration class: XLMForMultipleChoice (XLM model)
  - XLMRobertaConfig configuration class: XLMRobertaForMultipleChoice (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLForMultipleChoice (XLM-RoBERTa-XL model)
  - XLNetConfig configuration class: XLNetForMultipleChoice (XLNet model)
  - XmodConfig configuration class: XmodForMultipleChoice (X-MOD model)
  - YosoConfig configuration class: YosoForMultipleChoice (YOSO model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForMultipleChoice

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForMultipleChoice.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertForMultipleChoice (ALBERT model)
- bert — BertForMultipleChoice (BERT model)
- big_bird — BigBirdForMultipleChoice (BigBird model)
- camembert — CamembertForMultipleChoice (CamemBERT model)
- canine — CanineForMultipleChoice (CANINE model)
- convbert — ConvBertForMultipleChoice (ConvBERT model)
- data2vec-text — Data2VecTextForMultipleChoice (Data2VecText model)
- deberta-v2 — DebertaV2ForMultipleChoice (DeBERTa-v2 model)
- distilbert — DistilBertForMultipleChoice (DistilBERT model)
- electra — ElectraForMultipleChoice (ELECTRA model)
- ernie — ErnieForMultipleChoice (ERNIE model)
- ernie_m — ErnieMForMultipleChoice (ErnieM model)
- flaubert — FlaubertForMultipleChoice (FlauBERT model)
- fnet — FNetForMultipleChoice (FNet model)
- funnel — FunnelForMultipleChoice (Funnel Transformer model)
- ibert — IBertForMultipleChoice (I-BERT model)
- longformer — LongformerForMultipleChoice (Longformer model)
- luke — LukeForMultipleChoice (LUKE model)
- mega — MegaForMultipleChoice (MEGA model)
- megatron-bert — MegatronBertForMultipleChoice (Megatron-BERT model)
- mobilebert — MobileBertForMultipleChoice (MobileBERT model)
- mpnet — MPNetForMultipleChoice (MPNet model)
- mra — MraForMultipleChoice (MRA model)
- nezha — NezhaForMultipleChoice (Nezha model)
- nystromformer — NystromformerForMultipleChoice (Nyströmformer model)
- qdqbert — QDQBertForMultipleChoice (QDQBert model)
- rembert — RemBertForMultipleChoice (RemBERT model)
- roberta — RobertaForMultipleChoice (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormForMultipleChoice (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertForMultipleChoice (RoCBert model)
- roformer — RoFormerForMultipleChoice (RoFormer model)
- squeezebert — SqueezeBertForMultipleChoice (SqueezeBERT model)
- xlm — XLMForMultipleChoice (XLM model)
- xlm-roberta — XLMRobertaForMultipleChoice (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForMultipleChoice (XLM-RoBERTa-XL model)
- xlnet — XLNetForMultipleChoice (XLNet model)
- xmod — XmodForMultipleChoice (X-MOD model)
- yoso — YosoForMultipleChoice (YOSO model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForMultipleChoice

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForMultipleChoice.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForMultipleChoice

###
class transformers.TFAutoModelForMultipleChoice

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: TFAlbertForMultipleChoice (ALBERT model)
  - BertConfig configuration class: TFBertForMultipleChoice (BERT model)
  - CamembertConfig configuration class: TFCamembertForMultipleChoice (CamemBERT model)
  - ConvBertConfig configuration class: TFConvBertForMultipleChoice (ConvBERT model)
  - DebertaV2Config configuration class: TFDebertaV2ForMultipleChoice (DeBERTa-v2 model)
  - DistilBertConfig configuration class: TFDistilBertForMultipleChoice (DistilBERT model)
  - ElectraConfig configuration class: TFElectraForMultipleChoice (ELECTRA model)
  - FlaubertConfig configuration class: TFFlaubertForMultipleChoice (FlauBERT model)
  - FunnelConfig configuration class: TFFunnelForMultipleChoice (Funnel Transformer model)
  - LongformerConfig configuration class: TFLongformerForMultipleChoice (Longformer model)
  - MPNetConfig configuration class: TFMPNetForMultipleChoice (MPNet model)
  - MobileBertConfig configuration class: TFMobileBertForMultipleChoice (MobileBERT model)
  - RemBertConfig configuration class: TFRemBertForMultipleChoice (RemBERT model)
  - RoFormerConfig configuration class: TFRoFormerForMultipleChoice (RoFormer model)
  - RobertaConfig configuration class: TFRobertaForMultipleChoice (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForMultipleChoice (RoBERTa-PreLayerNorm model)
  - XLMConfig configuration class: TFXLMForMultipleChoice (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaForMultipleChoice (XLM-RoBERTa model)
  - XLNetConfig configuration class: TFXLNetForMultipleChoice (XLNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForMultipleChoice

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForMultipleChoice.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — TFAlbertForMultipleChoice (ALBERT model)
- bert — TFBertForMultipleChoice (BERT model)
- camembert — TFCamembertForMultipleChoice (CamemBERT model)
- convbert — TFConvBertForMultipleChoice (ConvBERT model)
- deberta-v2 — TFDebertaV2ForMultipleChoice (DeBERTa-v2 model)
- distilbert — TFDistilBertForMultipleChoice (DistilBERT model)
- electra — TFElectraForMultipleChoice (ELECTRA model)
- flaubert — TFFlaubertForMultipleChoice (FlauBERT model)
- funnel — TFFunnelForMultipleChoice (Funnel Transformer model)
- longformer — TFLongformerForMultipleChoice (Longformer model)
- mobilebert — TFMobileBertForMultipleChoice (MobileBERT model)
- mpnet — TFMPNetForMultipleChoice (MPNet model)
- rembert — TFRemBertForMultipleChoice (RemBERT model)
- roberta — TFRobertaForMultipleChoice (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormForMultipleChoice (RoBERTa-PreLayerNorm model)
- roformer — TFRoFormerForMultipleChoice (RoFormer model)
- xlm — TFXLMForMultipleChoice (XLM model)
- xlm-roberta — TFXLMRobertaForMultipleChoice (XLM-RoBERTa model)
- xlnet — TFXLNetForMultipleChoice (XLNet model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForMultipleChoice

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForMultipleChoice.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForMultipleChoice

###
class transformers.FlaxAutoModelForMultipleChoice

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: FlaxAlbertForMultipleChoice (ALBERT model)
  - BertConfig configuration class: FlaxBertForMultipleChoice (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdForMultipleChoice (BigBird model)
  - DistilBertConfig configuration class: FlaxDistilBertForMultipleChoice (DistilBERT model)
  - ElectraConfig configuration class: FlaxElectraForMultipleChoice (ELECTRA model)
  - RoFormerConfig configuration class: FlaxRoFormerForMultipleChoice (RoFormer model)
  - RobertaConfig configuration class: FlaxRobertaForMultipleChoice (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormForMultipleChoice (RoBERTa-PreLayerNorm model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaForMultipleChoice (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForMultipleChoice.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — FlaxAlbertForMultipleChoice (ALBERT model)
- bert — FlaxBertForMultipleChoice (BERT model)
- big_bird — FlaxBigBirdForMultipleChoice (BigBird model)
- distilbert — FlaxDistilBertForMultipleChoice (DistilBERT model)
- electra — FlaxElectraForMultipleChoice (ELECTRA model)
- roberta — FlaxRobertaForMultipleChoice (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormForMultipleChoice (RoBERTa-PreLayerNorm model)
- roformer — FlaxRoFormerForMultipleChoice (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForMultipleChoice (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForMultipleChoice

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForMultipleChoice.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForNextSentencePrediction

###
class transformers.AutoModelForNextSentencePrediction

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BertConfig configuration class: BertForNextSentencePrediction (BERT model)
  - ErnieConfig configuration class: ErnieForNextSentencePrediction (ERNIE model)
  - FNetConfig configuration class: FNetForNextSentencePrediction (FNet model)
  - MegatronBertConfig configuration class: MegatronBertForNextSentencePrediction (Megatron-BERT model)
  - MobileBertConfig configuration class: MobileBertForNextSentencePrediction (MobileBERT model)
  - NezhaConfig configuration class: NezhaForNextSentencePrediction (Nezha model)
  - QDQBertConfig configuration class: QDQBertForNextSentencePrediction (QDQBert model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForNextSentencePrediction

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForNextSentencePrediction.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bert — BertForNextSentencePrediction (BERT model)
- ernie — ErnieForNextSentencePrediction (ERNIE model)
- fnet — FNetForNextSentencePrediction (FNet model)
- megatron-bert — MegatronBertForNextSentencePrediction (Megatron-BERT model)
- mobilebert — MobileBertForNextSentencePrediction (MobileBERT model)
- nezha — NezhaForNextSentencePrediction (Nezha model)
- qdqbert — QDQBertForNextSentencePrediction (QDQBert model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForNextSentencePrediction

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForNextSentencePrediction.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForNextSentencePrediction

###
class transformers.TFAutoModelForNextSentencePrediction

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BertConfig configuration class: TFBertForNextSentencePrediction (BERT model)
  - MobileBertConfig configuration class: TFMobileBertForNextSentencePrediction (MobileBERT model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForNextSentencePrediction.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bert — TFBertForNextSentencePrediction (BERT model)
- mobilebert — TFMobileBertForNextSentencePrediction (MobileBERT model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForNextSentencePrediction

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForNextSentencePrediction.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForNextSentencePrediction

###
class transformers.FlaxAutoModelForNextSentencePrediction

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BertConfig configuration class: FlaxBertForNextSentencePrediction (BERT model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForNextSentencePrediction.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- bert — FlaxBertForNextSentencePrediction (BERT model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForNextSentencePrediction

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForTokenClassification

###
class transformers.AutoModelForTokenClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: AlbertForTokenClassification (ALBERT model)
  - BertConfig configuration class: BertForTokenClassification (BERT model)
  - BigBirdConfig configuration class: BigBirdForTokenClassification (BigBird model)
  - BioGptConfig configuration class: BioGptForTokenClassification (BioGpt model)
  - BloomConfig configuration class: BloomForTokenClassification (BLOOM model)
  - BrosConfig configuration class: BrosForTokenClassification (BROS model)
  - CamembertConfig configuration class: CamembertForTokenClassification (CamemBERT model)
  - CanineConfig configuration class: CanineForTokenClassification (CANINE model)
  - ConvBertConfig configuration class: ConvBertForTokenClassification (ConvBERT model)
  - Data2VecTextConfig configuration class: Data2VecTextForTokenClassification (Data2VecText model)
  - DebertaConfig configuration class: DebertaForTokenClassification (DeBERTa model)
  - DebertaV2Config configuration class: DebertaV2ForTokenClassification (DeBERTa-v2 model)
  - DistilBertConfig configuration class: DistilBertForTokenClassification (DistilBERT model)
  - ElectraConfig configuration class: ElectraForTokenClassification (ELECTRA model)
  - ErnieConfig configuration class: ErnieForTokenClassification (ERNIE model)
  - ErnieMConfig configuration class: ErnieMForTokenClassification (ErnieM model)
  - EsmConfig configuration class: EsmForTokenClassification (ESM model)
  - FNetConfig configuration class: FNetForTokenClassification (FNet model)
  - FalconConfig configuration class: FalconForTokenClassification (Falcon model)
  - FlaubertConfig configuration class: FlaubertForTokenClassification (FlauBERT model)
  - FunnelConfig configuration class: FunnelForTokenClassification (Funnel Transformer model)
  - GPT2Config configuration class: GPT2ForTokenClassification (OpenAI GPT-2 model)
  - GPTBigCodeConfig configuration class: GPTBigCodeForTokenClassification (GPTBigCode model)
  - GPTNeoConfig configuration class: GPTNeoForTokenClassification (GPT Neo model)
  - GPTNeoXConfig configuration class: GPTNeoXForTokenClassification (GPT NeoX model)
  - IBertConfig configuration class: IBertForTokenClassification (I-BERT model)
  - LayoutLMConfig configuration class: LayoutLMForTokenClassification (LayoutLM model)
  - LayoutLMv2Config configuration class: LayoutLMv2ForTokenClassification (LayoutLMv2 model)
  - LayoutLMv3Config configuration class: LayoutLMv3ForTokenClassification (LayoutLMv3 model)
  - LiltConfig configuration class: LiltForTokenClassification (LiLT model)
  - LongformerConfig configuration class: LongformerForTokenClassification (Longformer model)
  - LukeConfig configuration class: LukeForTokenClassification (LUKE model)
  - MPNetConfig configuration class: MPNetForTokenClassification (MPNet model)
  - MT5Config configuration class: MT5ForTokenClassification (MT5 model)
  - MarkupLMConfig configuration class: MarkupLMForTokenClassification (MarkupLM model)
  - MegaConfig configuration class: MegaForTokenClassification (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertForTokenClassification (Megatron-BERT model)
  - MobileBertConfig configuration class: MobileBertForTokenClassification (MobileBERT model)
  - MptConfig configuration class: MptForTokenClassification (MPT model)
  - MraConfig configuration class: MraForTokenClassification (MRA model)
  - NezhaConfig configuration class: NezhaForTokenClassification (Nezha model)
  - NystromformerConfig configuration class: NystromformerForTokenClassification (Nyströmformer model)
  - PhiConfig configuration class: PhiForTokenClassification (Phi model)
  - QDQBertConfig configuration class: QDQBertForTokenClassification (QDQBert model)
  - RemBertConfig configuration class: RemBertForTokenClassification (RemBERT model)
  - RoCBertConfig configuration class: RoCBertForTokenClassification (RoCBert model)
  - RoFormerConfig configuration class: RoFormerForTokenClassification (RoFormer model)
  - RobertaConfig configuration class: RobertaForTokenClassification (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)
  - SqueezeBertConfig configuration class: SqueezeBertForTokenClassification (SqueezeBERT model)
  - T5Config configuration class: T5ForTokenClassification (T5 model)
  - UMT5Config configuration class: UMT5ForTokenClassification (UMT5 model)
  - XLMConfig configuration class: XLMForTokenClassification (XLM model)
  - XLMRobertaConfig configuration class: XLMRobertaForTokenClassification (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLForTokenClassification (XLM-RoBERTa-XL model)
  - XLNetConfig configuration class: XLNetForTokenClassification (XLNet model)
  - XmodConfig configuration class: XmodForTokenClassification (X-MOD model)
  - YosoConfig configuration class: YosoForTokenClassification (YOSO model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a token classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForTokenClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForTokenClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertForTokenClassification (ALBERT model)
- bert — BertForTokenClassification (BERT model)
- big_bird — BigBirdForTokenClassification (BigBird model)
- biogpt — BioGptForTokenClassification (BioGpt model)
- bloom — BloomForTokenClassification (BLOOM model)
- bros — BrosForTokenClassification (BROS model)
- camembert — CamembertForTokenClassification (CamemBERT model)
- canine — CanineForTokenClassification (CANINE model)
- convbert — ConvBertForTokenClassification (ConvBERT model)
- data2vec-text — Data2VecTextForTokenClassification (Data2VecText model)
- deberta — DebertaForTokenClassification (DeBERTa model)
- deberta-v2 — DebertaV2ForTokenClassification (DeBERTa-v2 model)
- distilbert — DistilBertForTokenClassification (DistilBERT model)
- electra — ElectraForTokenClassification (ELECTRA model)
- ernie — ErnieForTokenClassification (ERNIE model)
- ernie_m — ErnieMForTokenClassification (ErnieM model)
- esm — EsmForTokenClassification (ESM model)
- falcon — FalconForTokenClassification (Falcon model)
- flaubert — FlaubertForTokenClassification (FlauBERT model)
- fnet — FNetForTokenClassification (FNet model)
- funnel — FunnelForTokenClassification (Funnel Transformer model)
- gpt-sw3 — GPT2ForTokenClassification (GPT-Sw3 model)
- gpt2 — GPT2ForTokenClassification (OpenAI GPT-2 model)
- gpt_bigcode — GPTBigCodeForTokenClassification (GPTBigCode model)
- gpt_neo — GPTNeoForTokenClassification (GPT Neo model)
- gpt_neox — GPTNeoXForTokenClassification (GPT NeoX model)
- ibert — IBertForTokenClassification (I-BERT model)
- layoutlm — LayoutLMForTokenClassification (LayoutLM model)
- layoutlmv2 — LayoutLMv2ForTokenClassification (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ForTokenClassification (LayoutLMv3 model)
- lilt — LiltForTokenClassification (LiLT model)
- longformer — LongformerForTokenClassification (Longformer model)
- luke — LukeForTokenClassification (LUKE model)
- markuplm — MarkupLMForTokenClassification (MarkupLM model)
- mega — MegaForTokenClassification (MEGA model)
- megatron-bert — MegatronBertForTokenClassification (Megatron-BERT model)
- mobilebert — MobileBertForTokenClassification (MobileBERT model)
- mpnet — MPNetForTokenClassification (MPNet model)
- mpt — MptForTokenClassification (MPT model)
- mra — MraForTokenClassification (MRA model)
- mt5 — MT5ForTokenClassification (MT5 model)
- nezha — NezhaForTokenClassification (Nezha model)
- nystromformer — NystromformerForTokenClassification (Nyströmformer model)
- phi — PhiForTokenClassification (Phi model)
- qdqbert — QDQBertForTokenClassification (QDQBert model)
- rembert — RemBertForTokenClassification (RemBERT model)
- roberta — RobertaForTokenClassification (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertForTokenClassification (RoCBert model)
- roformer — RoFormerForTokenClassification (RoFormer model)
- squeezebert — SqueezeBertForTokenClassification (SqueezeBERT model)
- t5 — T5ForTokenClassification (T5 model)
- umt5 — UMT5ForTokenClassification (UMT5 model)
- xlm — XLMForTokenClassification (XLM model)
- xlm-roberta — XLMRobertaForTokenClassification (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForTokenClassification (XLM-RoBERTa-XL model)
- xlnet — XLNetForTokenClassification (XLNet model)
- xmod — XmodForTokenClassification (X-MOD model)
- yoso — YosoForTokenClassification (YOSO model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForTokenClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForTokenClassification.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForTokenClassification

###
class transformers.TFAutoModelForTokenClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: TFAlbertForTokenClassification (ALBERT model)
  - BertConfig configuration class: TFBertForTokenClassification (BERT model)
  - CamembertConfig configuration class: TFCamembertForTokenClassification (CamemBERT model)
  - ConvBertConfig configuration class: TFConvBertForTokenClassification (ConvBERT model)
  - DebertaConfig configuration class: TFDebertaForTokenClassification (DeBERTa model)
  - DebertaV2Config configuration class: TFDebertaV2ForTokenClassification (DeBERTa-v2 model)
  - DistilBertConfig configuration class: TFDistilBertForTokenClassification (DistilBERT model)
  - ElectraConfig configuration class: TFElectraForTokenClassification (ELECTRA model)
  - EsmConfig configuration class: TFEsmForTokenClassification (ESM model)
  - FlaubertConfig configuration class: TFFlaubertForTokenClassification (FlauBERT model)
  - FunnelConfig configuration class: TFFunnelForTokenClassification (Funnel Transformer model)
  - LayoutLMConfig configuration class: TFLayoutLMForTokenClassification (LayoutLM model)
  - LayoutLMv3Config configuration class: TFLayoutLMv3ForTokenClassification (LayoutLMv3 model)
  - LongformerConfig configuration class: TFLongformerForTokenClassification (Longformer model)
  - MPNetConfig configuration class: TFMPNetForTokenClassification (MPNet model)
  - MobileBertConfig configuration class: TFMobileBertForTokenClassification (MobileBERT model)
  - RemBertConfig configuration class: TFRemBertForTokenClassification (RemBERT model)
  - RoFormerConfig configuration class: TFRoFormerForTokenClassification (RoFormer model)
  - RobertaConfig configuration class: TFRobertaForTokenClassification (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)
  - XLMConfig configuration class: TFXLMForTokenClassification (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaForTokenClassification (XLM-RoBERTa model)
  - XLNetConfig configuration class: TFXLNetForTokenClassification (XLNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a token classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForTokenClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForTokenClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — TFAlbertForTokenClassification (ALBERT model)
- bert — TFBertForTokenClassification (BERT model)
- camembert — TFCamembertForTokenClassification (CamemBERT model)
- convbert — TFConvBertForTokenClassification (ConvBERT model)
- deberta — TFDebertaForTokenClassification (DeBERTa model)
- deberta-v2 — TFDebertaV2ForTokenClassification (DeBERTa-v2 model)
- distilbert — TFDistilBertForTokenClassification (DistilBERT model)
- electra — TFElectraForTokenClassification (ELECTRA model)
- esm — TFEsmForTokenClassification (ESM model)
- flaubert — TFFlaubertForTokenClassification (FlauBERT model)
- funnel — TFFunnelForTokenClassification (Funnel Transformer model)
- layoutlm — TFLayoutLMForTokenClassification (LayoutLM model)
- layoutlmv3 — TFLayoutLMv3ForTokenClassification (LayoutLMv3 model)
- longformer — TFLongformerForTokenClassification (Longformer model)
- mobilebert — TFMobileBertForTokenClassification (MobileBERT model)
- mpnet — TFMPNetForTokenClassification (MPNet model)
- rembert — TFRemBertForTokenClassification (RemBERT model)
- roberta — TFRobertaForTokenClassification (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)
- roformer — TFRoFormerForTokenClassification (RoFormer model)
- xlm — TFXLMForTokenClassification (XLM model)
- xlm-roberta — TFXLMRobertaForTokenClassification (XLM-RoBERTa model)
- xlnet — TFXLNetForTokenClassification (XLNet model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForTokenClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForTokenClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForTokenClassification

###
class transformers.FlaxAutoModelForTokenClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: FlaxAlbertForTokenClassification (ALBERT model)
  - BertConfig configuration class: FlaxBertForTokenClassification (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdForTokenClassification (BigBird model)
  - DistilBertConfig configuration class: FlaxDistilBertForTokenClassification (DistilBERT model)
  - ElectraConfig configuration class: FlaxElectraForTokenClassification (ELECTRA model)
  - RoFormerConfig configuration class: FlaxRoFormerForTokenClassification (RoFormer model)
  - RobertaConfig configuration class: FlaxRobertaForTokenClassification (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaForTokenClassification (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a token classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForTokenClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForTokenClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — FlaxAlbertForTokenClassification (ALBERT model)
- bert — FlaxBertForTokenClassification (BERT model)
- big_bird — FlaxBigBirdForTokenClassification (BigBird model)
- distilbert — FlaxDistilBertForTokenClassification (DistilBERT model)
- electra — FlaxElectraForTokenClassification (ELECTRA model)
- roberta — FlaxRobertaForTokenClassification (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormForTokenClassification (RoBERTa-PreLayerNorm model)
- roformer — FlaxRoFormerForTokenClassification (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForTokenClassification (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForTokenClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForTokenClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForTokenClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForQuestionAnswering

###
class transformers.AutoModelForQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: AlbertForQuestionAnswering (ALBERT model)
  - BartConfig configuration class: BartForQuestionAnswering (BART model)
  - BertConfig configuration class: BertForQuestionAnswering (BERT model)
  - BigBirdConfig configuration class: BigBirdForQuestionAnswering (BigBird model)
  - BigBirdPegasusConfig configuration class: BigBirdPegasusForQuestionAnswering (BigBird-Pegasus model)
  - BloomConfig configuration class: BloomForQuestionAnswering (BLOOM model)
  - CamembertConfig configuration class: CamembertForQuestionAnswering (CamemBERT model)
  - CanineConfig configuration class: CanineForQuestionAnswering (CANINE model)
  - ConvBertConfig configuration class: ConvBertForQuestionAnswering (ConvBERT model)
  - Data2VecTextConfig configuration class: Data2VecTextForQuestionAnswering (Data2VecText model)
  - DebertaConfig configuration class: DebertaForQuestionAnswering (DeBERTa model)
  - DebertaV2Config configuration class: DebertaV2ForQuestionAnswering (DeBERTa-v2 model)
  - DistilBertConfig configuration class: DistilBertForQuestionAnswering (DistilBERT model)
  - ElectraConfig configuration class: ElectraForQuestionAnswering (ELECTRA model)
  - ErnieConfig configuration class: ErnieForQuestionAnswering (ERNIE model)
  - ErnieMConfig configuration class: ErnieMForQuestionAnswering (ErnieM model)
  - FNetConfig configuration class: FNetForQuestionAnswering (FNet model)
  - FalconConfig configuration class: FalconForQuestionAnswering (Falcon model)
  - FlaubertConfig configuration class: FlaubertForQuestionAnsweringSimple (FlauBERT model)
  - FunnelConfig configuration class: FunnelForQuestionAnswering (Funnel Transformer model)
  - GPT2Config configuration class: GPT2ForQuestionAnswering (OpenAI GPT-2 model)
  - GPTJConfig configuration class: GPTJForQuestionAnswering (GPT-J model)
  - GPTNeoConfig configuration class: GPTNeoForQuestionAnswering (GPT Neo model)
  - GPTNeoXConfig configuration class: GPTNeoXForQuestionAnswering (GPT NeoX model)
  - IBertConfig configuration class: IBertForQuestionAnswering (I-BERT model)
  - LEDConfig configuration class: LEDForQuestionAnswering (LED model)
  - LayoutLMv2Config configuration class: LayoutLMv2ForQuestionAnswering (LayoutLMv2 model)
  - LayoutLMv3Config configuration class: LayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
  - LiltConfig configuration class: LiltForQuestionAnswering (LiLT model)
  - LlamaConfig configuration class: LlamaForQuestionAnswering (LLaMA model)
  - LongformerConfig configuration class: LongformerForQuestionAnswering (Longformer model)
  - LukeConfig configuration class: LukeForQuestionAnswering (LUKE model)
  - LxmertConfig configuration class: LxmertForQuestionAnswering (LXMERT model)
  - MBartConfig configuration class: MBartForQuestionAnswering (mBART model)
  - MPNetConfig configuration class: MPNetForQuestionAnswering (MPNet model)
  - MT5Config configuration class: MT5ForQuestionAnswering (MT5 model)
  - MarkupLMConfig configuration class: MarkupLMForQuestionAnswering (MarkupLM model)
  - MegaConfig configuration class: MegaForQuestionAnswering (MEGA model)
  - MegatronBertConfig configuration class: MegatronBertForQuestionAnswering (Megatron-BERT model)
  - MobileBertConfig configuration class: MobileBertForQuestionAnswering (MobileBERT model)
  - MptConfig configuration class: MptForQuestionAnswering (MPT model)
  - MraConfig configuration class: MraForQuestionAnswering (MRA model)
  - MvpConfig configuration class: MvpForQuestionAnswering (MVP model)
  - NezhaConfig configuration class: NezhaForQuestionAnswering (Nezha model)
  - NystromformerConfig configuration class: NystromformerForQuestionAnswering (Nyströmformer model)
  - OPTConfig configuration class: OPTForQuestionAnswering (OPT model)
  - QDQBertConfig configuration class: QDQBertForQuestionAnswering (QDQBert model)
  - ReformerConfig configuration class: ReformerForQuestionAnswering (Reformer model)
  - RemBertConfig configuration class: RemBertForQuestionAnswering (RemBERT model)
  - RoCBertConfig configuration class: RoCBertForQuestionAnswering (RoCBert model)
  - RoFormerConfig configuration class: RoFormerForQuestionAnswering (RoFormer model)
  - RobertaConfig configuration class: RobertaForQuestionAnswering (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: RobertaPreLayerNormForQuestionAnswering (RoBERTa-PreLayerNorm model)
  - SplinterConfig configuration class: SplinterForQuestionAnswering (Splinter model)
  - SqueezeBertConfig configuration class: SqueezeBertForQuestionAnswering (SqueezeBERT model)
  - T5Config configuration class: T5ForQuestionAnswering (T5 model)
  - UMT5Config configuration class: UMT5ForQuestionAnswering (UMT5 model)
  - XLMConfig configuration class: XLMForQuestionAnsweringSimple (XLM model)
  - XLMRobertaConfig configuration class: XLMRobertaForQuestionAnswering (XLM-RoBERTa model)
  - XLMRobertaXLConfig configuration class: XLMRobertaXLForQuestionAnswering (XLM-RoBERTa-XL model)
  - XLNetConfig configuration class: XLNetForQuestionAnsweringSimple (XLNet model)
  - XmodConfig configuration class: XmodForQuestionAnswering (X-MOD model)
  - YosoConfig configuration class: YosoForQuestionAnswering (YOSO model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — AlbertForQuestionAnswering (ALBERT model)
- bart — BartForQuestionAnswering (BART model)
- bert — BertForQuestionAnswering (BERT model)
- big_bird — BigBirdForQuestionAnswering (BigBird model)
- bigbird_pegasus — BigBirdPegasusForQuestionAnswering (BigBird-Pegasus model)
- bloom — BloomForQuestionAnswering (BLOOM model)
- camembert — CamembertForQuestionAnswering (CamemBERT model)
- canine — CanineForQuestionAnswering (CANINE model)
- convbert — ConvBertForQuestionAnswering (ConvBERT model)
- data2vec-text — Data2VecTextForQuestionAnswering (Data2VecText model)
- deberta — DebertaForQuestionAnswering (DeBERTa model)
- deberta-v2 — DebertaV2ForQuestionAnswering (DeBERTa-v2 model)
- distilbert — DistilBertForQuestionAnswering (DistilBERT model)
- electra — ElectraForQuestionAnswering (ELECTRA model)
- ernie — ErnieForQuestionAnswering (ERNIE model)
- ernie_m — ErnieMForQuestionAnswering (ErnieM model)
- falcon — FalconForQuestionAnswering (Falcon model)
- flaubert — FlaubertForQuestionAnsweringSimple (FlauBERT model)
- fnet — FNetForQuestionAnswering (FNet model)
- funnel — FunnelForQuestionAnswering (Funnel Transformer model)
- gpt2 — GPT2ForQuestionAnswering (OpenAI GPT-2 model)
- gpt_neo — GPTNeoForQuestionAnswering (GPT Neo model)
- gpt_neox — GPTNeoXForQuestionAnswering (GPT NeoX model)
- gptj — GPTJForQuestionAnswering (GPT-J model)
- ibert — IBertForQuestionAnswering (I-BERT model)
- layoutlmv2 — LayoutLMv2ForQuestionAnswering (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
- led — LEDForQuestionAnswering (LED model)
- lilt — LiltForQuestionAnswering (LiLT model)
- llama — LlamaForQuestionAnswering (LLaMA model)
- longformer — LongformerForQuestionAnswering (Longformer model)
- luke — LukeForQuestionAnswering (LUKE model)
- lxmert — LxmertForQuestionAnswering (LXMERT model)
- markuplm — MarkupLMForQuestionAnswering (MarkupLM model)
- mbart — MBartForQuestionAnswering (mBART model)
- mega — MegaForQuestionAnswering (MEGA model)
- megatron-bert — MegatronBertForQuestionAnswering (Megatron-BERT model)
- mobilebert — MobileBertForQuestionAnswering (MobileBERT model)
- mpnet — MPNetForQuestionAnswering (MPNet model)
- mpt — MptForQuestionAnswering (MPT model)
- mra — MraForQuestionAnswering (MRA model)
- mt5 — MT5ForQuestionAnswering (MT5 model)
- mvp — MvpForQuestionAnswering (MVP model)
- nezha — NezhaForQuestionAnswering (Nezha model)
- nystromformer — NystromformerForQuestionAnswering (Nyströmformer model)
- opt — OPTForQuestionAnswering (OPT model)
- qdqbert — QDQBertForQuestionAnswering (QDQBert model)
- reformer — ReformerForQuestionAnswering (Reformer model)
- rembert — RemBertForQuestionAnswering (RemBERT model)
- roberta — RobertaForQuestionAnswering (RoBERTa model)
- roberta-prelayernorm — RobertaPreLayerNormForQuestionAnswering (RoBERTa-PreLayerNorm model)
- roc_bert — RoCBertForQuestionAnswering (RoCBert model)
- roformer — RoFormerForQuestionAnswering (RoFormer model)
- splinter — SplinterForQuestionAnswering (Splinter model)
- squeezebert — SqueezeBertForQuestionAnswering (SqueezeBERT model)
- t5 — T5ForQuestionAnswering (T5 model)
- umt5 — UMT5ForQuestionAnswering (UMT5 model)
- xlm — XLMForQuestionAnsweringSimple (XLM model)
- xlm-roberta — XLMRobertaForQuestionAnswering (XLM-RoBERTa model)
- xlm-roberta-xl — XLMRobertaXLForQuestionAnswering (XLM-RoBERTa-XL model)
- xlnet — XLNetForQuestionAnsweringSimple (XLNet model)
- xmod — XmodForQuestionAnswering (X-MOD model)
- yoso — YosoForQuestionAnswering (YOSO model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForQuestionAnswering.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForQuestionAnswering

###
class transformers.TFAutoModelForQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: TFAlbertForQuestionAnswering (ALBERT model)
  - BertConfig configuration class: TFBertForQuestionAnswering (BERT model)
  - CamembertConfig configuration class: TFCamembertForQuestionAnswering (CamemBERT model)
  - ConvBertConfig configuration class: TFConvBertForQuestionAnswering (ConvBERT model)
  - DebertaConfig configuration class: TFDebertaForQuestionAnswering (DeBERTa model)
  - DebertaV2Config configuration class: TFDebertaV2ForQuestionAnswering (DeBERTa-v2 model)
  - DistilBertConfig configuration class: TFDistilBertForQuestionAnswering (DistilBERT model)
  - ElectraConfig configuration class: TFElectraForQuestionAnswering (ELECTRA model)
  - FlaubertConfig configuration class: TFFlaubertForQuestionAnsweringSimple (FlauBERT model)
  - FunnelConfig configuration class: TFFunnelForQuestionAnswering (Funnel Transformer model)
  - GPTJConfig configuration class: TFGPTJForQuestionAnswering (GPT-J model)
  - LayoutLMv3Config configuration class: TFLayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
  - LongformerConfig configuration class: TFLongformerForQuestionAnswering (Longformer model)
  - MPNetConfig configuration class: TFMPNetForQuestionAnswering (MPNet model)
  - MobileBertConfig configuration class: TFMobileBertForQuestionAnswering (MobileBERT model)
  - RemBertConfig configuration class: TFRemBertForQuestionAnswering (RemBERT model)
  - RoFormerConfig configuration class: TFRoFormerForQuestionAnswering (RoFormer model)
  - RobertaConfig configuration class: TFRobertaForQuestionAnswering (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: TFRobertaPreLayerNormForQuestionAnswering (RoBERTa-PreLayerNorm model)
  - XLMConfig configuration class: TFXLMForQuestionAnsweringSimple (XLM model)
  - XLMRobertaConfig configuration class: TFXLMRobertaForQuestionAnswering (XLM-RoBERTa model)
  - XLNetConfig configuration class: TFXLNetForQuestionAnsweringSimple (XLNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — TFAlbertForQuestionAnswering (ALBERT model)
- bert — TFBertForQuestionAnswering (BERT model)
- camembert — TFCamembertForQuestionAnswering (CamemBERT model)
- convbert — TFConvBertForQuestionAnswering (ConvBERT model)
- deberta — TFDebertaForQuestionAnswering (DeBERTa model)
- deberta-v2 — TFDebertaV2ForQuestionAnswering (DeBERTa-v2 model)
- distilbert — TFDistilBertForQuestionAnswering (DistilBERT model)
- electra — TFElectraForQuestionAnswering (ELECTRA model)
- flaubert — TFFlaubertForQuestionAnsweringSimple (FlauBERT model)
- funnel — TFFunnelForQuestionAnswering (Funnel Transformer model)
- gptj — TFGPTJForQuestionAnswering (GPT-J model)
- layoutlmv3 — TFLayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
- longformer — TFLongformerForQuestionAnswering (Longformer model)
- mobilebert — TFMobileBertForQuestionAnswering (MobileBERT model)
- mpnet — TFMPNetForQuestionAnswering (MPNet model)
- rembert — TFRemBertForQuestionAnswering (RemBERT model)
- roberta — TFRobertaForQuestionAnswering (RoBERTa model)
- roberta-prelayernorm — TFRobertaPreLayerNormForQuestionAnswering (RoBERTa-PreLayerNorm model)
- roformer — TFRoFormerForQuestionAnswering (RoFormer model)
- xlm — TFXLMForQuestionAnsweringSimple (XLM model)
- xlm-roberta — TFXLMRobertaForQuestionAnswering (XLM-RoBERTa model)
- xlnet — TFXLNetForQuestionAnsweringSimple (XLNet model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForQuestionAnswering.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForQuestionAnswering

###
class transformers.FlaxAutoModelForQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlbertConfig configuration class: FlaxAlbertForQuestionAnswering (ALBERT model)
  - BartConfig configuration class: FlaxBartForQuestionAnswering (BART model)
  - BertConfig configuration class: FlaxBertForQuestionAnswering (BERT model)
  - BigBirdConfig configuration class: FlaxBigBirdForQuestionAnswering (BigBird model)
  - DistilBertConfig configuration class: FlaxDistilBertForQuestionAnswering (DistilBERT model)
  - ElectraConfig configuration class: FlaxElectraForQuestionAnswering (ELECTRA model)
  - MBartConfig configuration class: FlaxMBartForQuestionAnswering (mBART model)
  - RoFormerConfig configuration class: FlaxRoFormerForQuestionAnswering (RoFormer model)
  - RobertaConfig configuration class: FlaxRobertaForQuestionAnswering (RoBERTa model)
  - RobertaPreLayerNormConfig configuration class: FlaxRobertaPreLayerNormForQuestionAnswering (RoBERTa-PreLayerNorm model)
  - XLMRobertaConfig configuration class: FlaxXLMRobertaForQuestionAnswering (XLM-RoBERTa model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- albert — FlaxAlbertForQuestionAnswering (ALBERT model)
- bart — FlaxBartForQuestionAnswering (BART model)
- bert — FlaxBertForQuestionAnswering (BERT model)
- big_bird — FlaxBigBirdForQuestionAnswering (BigBird model)
- distilbert — FlaxDistilBertForQuestionAnswering (DistilBERT model)
- electra — FlaxElectraForQuestionAnswering (ELECTRA model)
- mbart — FlaxMBartForQuestionAnswering (mBART model)
- roberta — FlaxRobertaForQuestionAnswering (RoBERTa model)
- roberta-prelayernorm — FlaxRobertaPreLayerNormForQuestionAnswering (RoBERTa-PreLayerNorm model)
- roformer — FlaxRoFormerForQuestionAnswering (RoFormer model)
- xlm-roberta — FlaxXLMRobertaForQuestionAnswering (XLM-RoBERTa model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForQuestionAnswering.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForQuestionAnswering.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForTextEncoding

###
class transformers.AutoModelForTextEncoding

< source >

( *args **kwargs )

###
TFAutoModelForTextEncoding

###
class transformers.TFAutoModelForTextEncoding

< source >

( *args **kwargs )

##
Computer vision

The following auto classes are available for the following computer vision tasks.

###
AutoModelForDepthEstimation

###
class transformers.AutoModelForDepthEstimation

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - DPTConfig configuration class: DPTForDepthEstimation (DPT model)
  - DepthAnythingConfig configuration class: DepthAnythingForDepthEstimation (Depth Anything model)
  - GLPNConfig configuration class: GLPNForDepthEstimation (GLPN model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a depth estimation head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForDepthEstimation

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForDepthEstimation.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- depth_anything — DepthAnythingForDepthEstimation (Depth Anything model)
- dpt — DPTForDepthEstimation (DPT model)
- glpn — GLPNForDepthEstimation (GLPN model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForDepthEstimation

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForDepthEstimation.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForDepthEstimation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForDepthEstimation.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForImageClassification

###
class transformers.AutoModelForImageClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BeitConfig configuration class: BeitForImageClassification (BEiT model)
  - BitConfig configuration class: BitForImageClassification (BiT model)
  - CLIPConfig configuration class: CLIPForImageClassification (CLIP model)
  - ConvNextConfig configuration class: ConvNextForImageClassification (ConvNeXT model)
  - ConvNextV2Config configuration class: ConvNextV2ForImageClassification (ConvNeXTV2 model)
  - CvtConfig configuration class: CvtForImageClassification (CvT model)
  - Data2VecVisionConfig configuration class: Data2VecVisionForImageClassification (Data2VecVision model)
  - DeiTConfig configuration class: DeiTForImageClassification or DeiTForImageClassificationWithTeacher (DeiT model)
  - DinatConfig configuration class: DinatForImageClassification (DiNAT model)
  - Dinov2Config configuration class: Dinov2ForImageClassification (DINOv2 model)
  - EfficientFormerConfig configuration class: EfficientFormerForImageClassification or EfficientFormerForImageClassificationWithTeacher (EfficientFormer model)
  - EfficientNetConfig configuration class: EfficientNetForImageClassification (EfficientNet model)
  - FocalNetConfig configuration class: FocalNetForImageClassification (FocalNet model)
  - ImageGPTConfig configuration class: ImageGPTForImageClassification (ImageGPT model)
  - LevitConfig configuration class: LevitForImageClassification or LevitForImageClassificationWithTeacher (LeViT model)
  - MobileNetV1Config configuration class: MobileNetV1ForImageClassification (MobileNetV1 model)
  - MobileNetV2Config configuration class: MobileNetV2ForImageClassification (MobileNetV2 model)
  - MobileViTConfig configuration class: MobileViTForImageClassification (MobileViT model)
  - MobileViTV2Config configuration class: MobileViTV2ForImageClassification (MobileViTV2 model)
  - NatConfig configuration class: NatForImageClassification (NAT model)
  - PerceiverConfig configuration class: PerceiverForImageClassificationLearned or PerceiverForImageClassificationFourier or PerceiverForImageClassificationConvProcessing (Perceiver model)
  - PoolFormerConfig configuration class: PoolFormerForImageClassification (PoolFormer model)
  - PvtConfig configuration class: PvtForImageClassification (PVT model)
  - RegNetConfig configuration class: RegNetForImageClassification (RegNet model)
  - ResNetConfig configuration class: ResNetForImageClassification (ResNet model)
  - SegformerConfig configuration class: SegformerForImageClassification (SegFormer model)
  - SiglipConfig configuration class: SiglipForImageClassification (SigLIP model)
  - SwiftFormerConfig configuration class: SwiftFormerForImageClassification (SwiftFormer model)
  - SwinConfig configuration class: SwinForImageClassification (Swin Transformer model)
  - Swinv2Config configuration class: Swinv2ForImageClassification (Swin Transformer V2 model)
  - VanConfig configuration class: VanForImageClassification (VAN model)
  - ViTConfig configuration class: ViTForImageClassification (ViT model)
  - ViTHybridConfig configuration class: ViTHybridForImageClassification (ViT Hybrid model)
  - ViTMSNConfig configuration class: ViTMSNForImageClassification (ViTMSN model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a image classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForImageClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForImageClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- beit — BeitForImageClassification (BEiT model)
- bit — BitForImageClassification (BiT model)
- clip — CLIPForImageClassification (CLIP model)
- convnext — ConvNextForImageClassification (ConvNeXT model)
- convnextv2 — ConvNextV2ForImageClassification (ConvNeXTV2 model)
- cvt — CvtForImageClassification (CvT model)
- data2vec-vision — Data2VecVisionForImageClassification (Data2VecVision model)
- deit — DeiTForImageClassification or DeiTForImageClassificationWithTeacher (DeiT model)
- dinat — DinatForImageClassification (DiNAT model)
- dinov2 — Dinov2ForImageClassification (DINOv2 model)
- efficientformer — EfficientFormerForImageClassification or EfficientFormerForImageClassificationWithTeacher (EfficientFormer model)
- efficientnet — EfficientNetForImageClassification (EfficientNet model)
- focalnet — FocalNetForImageClassification (FocalNet model)
- imagegpt — ImageGPTForImageClassification (ImageGPT model)
- levit — LevitForImageClassification or LevitForImageClassificationWithTeacher (LeViT model)
- mobilenet_v1 — MobileNetV1ForImageClassification (MobileNetV1 model)
- mobilenet_v2 — MobileNetV2ForImageClassification (MobileNetV2 model)
- mobilevit — MobileViTForImageClassification (MobileViT model)
- mobilevitv2 — MobileViTV2ForImageClassification (MobileViTV2 model)
- nat — NatForImageClassification (NAT model)
- perceiver — PerceiverForImageClassificationLearned or PerceiverForImageClassificationFourier or PerceiverForImageClassificationConvProcessing (Perceiver model)
- poolformer — PoolFormerForImageClassification (PoolFormer model)
- pvt — PvtForImageClassification (PVT model)
- regnet — RegNetForImageClassification (RegNet model)
- resnet — ResNetForImageClassification (ResNet model)
- segformer — SegformerForImageClassification (SegFormer model)
- siglip — SiglipForImageClassification (SigLIP model)
- swiftformer — SwiftFormerForImageClassification (SwiftFormer model)
- swin — SwinForImageClassification (Swin Transformer model)
- swinv2 — Swinv2ForImageClassification (Swin Transformer V2 model)
- van — VanForImageClassification (VAN model)
- vit — ViTForImageClassification (ViT model)
- vit_hybrid — ViTHybridForImageClassification (ViT Hybrid model)
- vit_msn — ViTMSNForImageClassification (ViTMSN model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForImageClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForImageClassification.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForImageClassification

###
class transformers.TFAutoModelForImageClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - ConvNextConfig configuration class: TFConvNextForImageClassification (ConvNeXT model)
  - ConvNextV2Config configuration class: TFConvNextV2ForImageClassification (ConvNeXTV2 model)
  - CvtConfig configuration class: TFCvtForImageClassification (CvT model)
  - Data2VecVisionConfig configuration class: TFData2VecVisionForImageClassification (Data2VecVision model)
  - DeiTConfig configuration class: TFDeiTForImageClassification or TFDeiTForImageClassificationWithTeacher (DeiT model)
  - EfficientFormerConfig configuration class: TFEfficientFormerForImageClassification or TFEfficientFormerForImageClassificationWithTeacher (EfficientFormer model)
  - MobileViTConfig configuration class: TFMobileViTForImageClassification (MobileViT model)
  - RegNetConfig configuration class: TFRegNetForImageClassification (RegNet model)
  - ResNetConfig configuration class: TFResNetForImageClassification (ResNet model)
  - SegformerConfig configuration class: TFSegformerForImageClassification (SegFormer model)
  - SwinConfig configuration class: TFSwinForImageClassification (Swin Transformer model)
  - ViTConfig configuration class: TFViTForImageClassification (ViT model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a image classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForImageClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForImageClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- convnext — TFConvNextForImageClassification (ConvNeXT model)
- convnextv2 — TFConvNextV2ForImageClassification (ConvNeXTV2 model)
- cvt — TFCvtForImageClassification (CvT model)
- data2vec-vision — TFData2VecVisionForImageClassification (Data2VecVision model)
- deit — TFDeiTForImageClassification or TFDeiTForImageClassificationWithTeacher (DeiT model)
- efficientformer — TFEfficientFormerForImageClassification or TFEfficientFormerForImageClassificationWithTeacher (EfficientFormer model)
- mobilevit — TFMobileViTForImageClassification (MobileViT model)
- regnet — TFRegNetForImageClassification (RegNet model)
- resnet — TFResNetForImageClassification (ResNet model)
- segformer — TFSegformerForImageClassification (SegFormer model)
- swin — TFSwinForImageClassification (Swin Transformer model)
- vit — TFViTForImageClassification (ViT model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForImageClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForImageClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForImageClassification

###
class transformers.FlaxAutoModelForImageClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BeitConfig configuration class: FlaxBeitForImageClassification (BEiT model)
  - RegNetConfig configuration class: FlaxRegNetForImageClassification (RegNet model)
  - ResNetConfig configuration class: FlaxResNetForImageClassification (ResNet model)
  - ViTConfig configuration class: FlaxViTForImageClassification (ViT model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a image classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForImageClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForImageClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- beit — FlaxBeitForImageClassification (BEiT model)
- regnet — FlaxRegNetForImageClassification (RegNet model)
- resnet — FlaxResNetForImageClassification (ResNet model)
- vit — FlaxViTForImageClassification (ViT model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForImageClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForImageClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForVideoClassification

###
class transformers.AutoModelForVideoClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - TimesformerConfig configuration class: TimesformerForVideoClassification (TimeSformer model)
  - VideoMAEConfig configuration class: VideoMAEForVideoClassification (VideoMAE model)
  - VivitConfig configuration class: VivitForVideoClassification (ViViT model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a video classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForVideoClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForVideoClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a video classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- timesformer — TimesformerForVideoClassification (TimeSformer model)
- videomae — VideoMAEForVideoClassification (VideoMAE model)
- vivit — VivitForVideoClassification (ViViT model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForVideoClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForVideoClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForVideoClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForVideoClassification.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForMaskedImageModeling

###
class transformers.AutoModelForMaskedImageModeling

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - DeiTConfig configuration class: DeiTForMaskedImageModeling (DeiT model)
  - FocalNetConfig configuration class: FocalNetForMaskedImageModeling (FocalNet model)
  - SwinConfig configuration class: SwinForMaskedImageModeling (Swin Transformer model)
  - Swinv2Config configuration class: Swinv2ForMaskedImageModeling (Swin Transformer V2 model)
  - ViTConfig configuration class: ViTForMaskedImageModeling (ViT model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForMaskedImageModeling

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForMaskedImageModeling.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- deit — DeiTForMaskedImageModeling (DeiT model)
- focalnet — FocalNetForMaskedImageModeling (FocalNet model)
- swin — SwinForMaskedImageModeling (Swin Transformer model)
- swinv2 — Swinv2ForMaskedImageModeling (Swin Transformer V2 model)
- vit — ViTForMaskedImageModeling (ViT model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForMaskedImageModeling

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForMaskedImageModeling.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForMaskedImageModeling

###
class transformers.TFAutoModelForMaskedImageModeling

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - DeiTConfig configuration class: TFDeiTForMaskedImageModeling (DeiT model)
  - SwinConfig configuration class: TFSwinForMaskedImageModeling (Swin Transformer model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForMaskedImageModeling

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForMaskedImageModeling.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- deit — TFDeiTForMaskedImageModeling (DeiT model)
- swin — TFSwinForMaskedImageModeling (Swin Transformer model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForMaskedImageModeling

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForMaskedImageModeling.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForMaskedImageModeling.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForObjectDetection

###
class transformers.AutoModelForObjectDetection

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - ConditionalDetrConfig configuration class: ConditionalDetrForObjectDetection (Conditional DETR model)
  - DeformableDetrConfig configuration class: DeformableDetrForObjectDetection (Deformable DETR model)
  - DetaConfig configuration class: DetaForObjectDetection (DETA model)
  - DetrConfig configuration class: DetrForObjectDetection (DETR model)
  - TableTransformerConfig configuration class: TableTransformerForObjectDetection (Table Transformer model)
  - YolosConfig configuration class: YolosForObjectDetection (YOLOS model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a object detection head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForObjectDetection

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForObjectDetection.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a object detection head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- conditional_detr — ConditionalDetrForObjectDetection (Conditional DETR model)
- deformable_detr — DeformableDetrForObjectDetection (Deformable DETR model)
- deta — DetaForObjectDetection (DETA model)
- detr — DetrForObjectDetection (DETR model)
- table-transformer — TableTransformerForObjectDetection (Table Transformer model)
- yolos — YolosForObjectDetection (YOLOS model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForObjectDetection

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForObjectDetection.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForObjectDetection.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForObjectDetection.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForImageSegmentation

###
class transformers.AutoModelForImageSegmentation

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - DetrConfig configuration class: DetrForSegmentation (DETR model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a image segmentation head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForImageSegmentation

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForImageSegmentation.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- detr — DetrForSegmentation (DETR model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForImageSegmentation

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForImageSegmentation.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForImageSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForImageSegmentation.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForImageToImage

###
class transformers.AutoModelForImageToImage

< source >

( *args **kwargs )

###
AutoModelForSemanticSegmentation

###
class transformers.AutoModelForSemanticSegmentation

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BeitConfig configuration class: BeitForSemanticSegmentation (BEiT model)
  - DPTConfig configuration class: DPTForSemanticSegmentation (DPT model)
  - Data2VecVisionConfig configuration class: Data2VecVisionForSemanticSegmentation (Data2VecVision model)
  - MobileNetV2Config configuration class: MobileNetV2ForSemanticSegmentation (MobileNetV2 model)
  - MobileViTConfig configuration class: MobileViTForSemanticSegmentation (MobileViT model)
  - MobileViTV2Config configuration class: MobileViTV2ForSemanticSegmentation (MobileViTV2 model)
  - SegformerConfig configuration class: SegformerForSemanticSegmentation (SegFormer model)
  - UperNetConfig configuration class: UperNetForSemanticSegmentation (UPerNet model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForSemanticSegmentation

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForSemanticSegmentation.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- beit — BeitForSemanticSegmentation (BEiT model)
- data2vec-vision — Data2VecVisionForSemanticSegmentation (Data2VecVision model)
- dpt — DPTForSemanticSegmentation (DPT model)
- mobilenet_v2 — MobileNetV2ForSemanticSegmentation (MobileNetV2 model)
- mobilevit — MobileViTForSemanticSegmentation (MobileViT model)
- mobilevitv2 — MobileViTV2ForSemanticSegmentation (MobileViTV2 model)
- segformer — SegformerForSemanticSegmentation (SegFormer model)
- upernet — UperNetForSemanticSegmentation (UPerNet model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForSemanticSegmentation

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForSemanticSegmentation.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForSemanticSegmentation

###
class transformers.TFAutoModelForSemanticSegmentation

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Data2VecVisionConfig configuration class: TFData2VecVisionForSemanticSegmentation (Data2VecVision model)
  - MobileViTConfig configuration class: TFMobileViTForSemanticSegmentation (MobileViT model)
  - SegformerConfig configuration class: TFSegformerForSemanticSegmentation (SegFormer model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForSemanticSegmentation.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- data2vec-vision — TFData2VecVisionForSemanticSegmentation (Data2VecVision model)
- mobilevit — TFMobileViTForSemanticSegmentation (MobileViT model)
- segformer — TFSegformerForSemanticSegmentation (SegFormer model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForSemanticSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForSemanticSegmentation.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForInstanceSegmentation

###
class transformers.AutoModelForInstanceSegmentation

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - MaskFormerConfig configuration class: MaskFormerForInstanceSegmentation (MaskFormer model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForInstanceSegmentation

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForInstanceSegmentation.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- maskformer — MaskFormerForInstanceSegmentation (MaskFormer model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForInstanceSegmentation

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForInstanceSegmentation.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForInstanceSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForInstanceSegmentation.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForUniversalSegmentation

###
class transformers.AutoModelForUniversalSegmentation

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - DetrConfig configuration class: DetrForSegmentation (DETR model)
  - Mask2FormerConfig configuration class: Mask2FormerForUniversalSegmentation (Mask2Former model)
  - MaskFormerConfig configuration class: MaskFormerForInstanceSegmentation (MaskFormer model)
  - OneFormerConfig configuration class: OneFormerForUniversalSegmentation (OneFormer model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForUniversalSegmentation

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForUniversalSegmentation.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- detr — DetrForSegmentation (DETR model)
- mask2former — Mask2FormerForUniversalSegmentation (Mask2Former model)
- maskformer — MaskFormerForInstanceSegmentation (MaskFormer model)
- oneformer — OneFormerForUniversalSegmentation (OneFormer model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForUniversalSegmentation

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForUniversalSegmentation.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForUniversalSegmentation.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForUniversalSegmentation.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForZeroShotImageClassification

###
class transformers.AutoModelForZeroShotImageClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - AlignConfig configuration class: AlignModel (ALIGN model)
  - AltCLIPConfig configuration class: AltCLIPModel (AltCLIP model)
  - BlipConfig configuration class: BlipModel (BLIP model)
  - CLIPConfig configuration class: CLIPModel (CLIP model)
  - CLIPSegConfig configuration class: CLIPSegModel (CLIPSeg model)
  - ChineseCLIPConfig configuration class: ChineseCLIPModel (Chinese-CLIP model)
  - SiglipConfig configuration class: SiglipModel (SigLIP model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForZeroShotImageClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForZeroShotImageClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- align — AlignModel (ALIGN model)
- altclip — AltCLIPModel (AltCLIP model)
- blip — BlipModel (BLIP model)
- chinese_clip — ChineseCLIPModel (Chinese-CLIP model)
- clip — CLIPModel (CLIP model)
- clipseg — CLIPSegModel (CLIPSeg model)
- siglip — SiglipModel (SigLIP model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForZeroShotImageClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForZeroShotImageClassification.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForZeroShotImageClassification

###
class transformers.TFAutoModelForZeroShotImageClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BlipConfig configuration class: TFBlipModel (BLIP model)
  - CLIPConfig configuration class: TFCLIPModel (CLIP model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForZeroShotImageClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForZeroShotImageClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- blip — TFBlipModel (BLIP model)
- clip — TFCLIPModel (CLIP model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForZeroShotImageClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForZeroShotImageClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForZeroShotImageClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForZeroShotObjectDetection

###
class transformers.AutoModelForZeroShotObjectDetection

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - OwlViTConfig configuration class: OwlViTForObjectDetection (OWL-ViT model)
  - Owlv2Config configuration class: Owlv2ForObjectDetection (OWLv2 model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForZeroShotObjectDetection.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- owlv2 — Owlv2ForObjectDetection (OWLv2 model)
- owlvit — OwlViTForObjectDetection (OWL-ViT model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForZeroShotObjectDetection

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForZeroShotObjectDetection.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForZeroShotObjectDetection.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForZeroShotObjectDetection.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

##
Audio

The following auto classes are available for the following audio tasks.

###
AutoModelForAudioClassification

###
class transformers.AutoModelForAudioClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - ASTConfig configuration class: ASTForAudioClassification (Audio Spectrogram Transformer model)
  - Data2VecAudioConfig configuration class: Data2VecAudioForSequenceClassification (Data2VecAudio model)
  - HubertConfig configuration class: HubertForSequenceClassification (Hubert model)
  - SEWConfig configuration class: SEWForSequenceClassification (SEW model)
  - SEWDConfig configuration class: SEWDForSequenceClassification (SEW-D model)
  - UniSpeechConfig configuration class: UniSpeechForSequenceClassification (UniSpeech model)
  - UniSpeechSatConfig configuration class: UniSpeechSatForSequenceClassification (UniSpeechSat model)
  - Wav2Vec2BertConfig configuration class: Wav2Vec2BertForSequenceClassification (Wav2Vec2-BERT model)
  - Wav2Vec2Config configuration class: Wav2Vec2ForSequenceClassification (Wav2Vec2 model)
  - Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForSequenceClassification (Wav2Vec2-Conformer model)
  - WavLMConfig configuration class: WavLMForSequenceClassification (WavLM model)
  - WhisperConfig configuration class: WhisperForAudioClassification (Whisper model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a audio classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForAudioClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForAudioClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- audio-spectrogram-transformer — ASTForAudioClassification (Audio Spectrogram Transformer model)
- data2vec-audio — Data2VecAudioForSequenceClassification (Data2VecAudio model)
- hubert — HubertForSequenceClassification (Hubert model)
- sew — SEWForSequenceClassification (SEW model)
- sew-d — SEWDForSequenceClassification (SEW-D model)
- unispeech — UniSpeechForSequenceClassification (UniSpeech model)
- unispeech-sat — UniSpeechSatForSequenceClassification (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForSequenceClassification (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2BertForSequenceClassification (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2ConformerForSequenceClassification (Wav2Vec2-Conformer model)
- wavlm — WavLMForSequenceClassification (WavLM model)
- whisper — WhisperForAudioClassification (Whisper model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForAudioClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForAudioClassification.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForAudioFrameClassification

###
class transformers.TFAutoModelForAudioClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Wav2Vec2Config configuration class: TFWav2Vec2ForSequenceClassification (Wav2Vec2 model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a audio classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForAudioClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForAudioClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- wav2vec2 — TFWav2Vec2ForSequenceClassification (Wav2Vec2 model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForAudioClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForAudioClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForAudioClassification.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
TFAutoModelForAudioFrameClassification

###
class transformers.AutoModelForAudioFrameClassification

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Data2VecAudioConfig configuration class: Data2VecAudioForAudioFrameClassification (Data2VecAudio model)
  - UniSpeechSatConfig configuration class: UniSpeechSatForAudioFrameClassification (UniSpeechSat model)
  - Wav2Vec2BertConfig configuration class: Wav2Vec2BertForAudioFrameClassification (Wav2Vec2-BERT model)
  - Wav2Vec2Config configuration class: Wav2Vec2ForAudioFrameClassification (Wav2Vec2 model)
  - Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForAudioFrameClassification (Wav2Vec2-Conformer model)
  - WavLMConfig configuration class: WavLMForAudioFrameClassification (WavLM model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForAudioFrameClassification

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForAudioFrameClassification.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- data2vec-audio — Data2VecAudioForAudioFrameClassification (Data2VecAudio model)
- unispeech-sat — UniSpeechSatForAudioFrameClassification (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForAudioFrameClassification (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2BertForAudioFrameClassification (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2ConformerForAudioFrameClassification (Wav2Vec2-Conformer model)
- wavlm — WavLMForAudioFrameClassification (WavLM model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForAudioFrameClassification

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForAudioFrameClassification.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForAudioFrameClassification.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForAudioFrameClassification.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForCTC

###
class transformers.AutoModelForCTC

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Data2VecAudioConfig configuration class: Data2VecAudioForCTC (Data2VecAudio model)
  - HubertConfig configuration class: HubertForCTC (Hubert model)
  - MCTCTConfig configuration class: MCTCTForCTC (M-CTC-T model)
  - SEWConfig configuration class: SEWForCTC (SEW model)
  - SEWDConfig configuration class: SEWDForCTC (SEW-D model)
  - UniSpeechConfig configuration class: UniSpeechForCTC (UniSpeech model)
  - UniSpeechSatConfig configuration class: UniSpeechSatForCTC (UniSpeechSat model)
  - Wav2Vec2BertConfig configuration class: Wav2Vec2BertForCTC (Wav2Vec2-BERT model)
  - Wav2Vec2Config configuration class: Wav2Vec2ForCTC (Wav2Vec2 model)
  - Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForCTC (Wav2Vec2-Conformer model)
  - WavLMConfig configuration class: WavLMForCTC (WavLM model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForCTC

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForCTC.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- data2vec-audio — Data2VecAudioForCTC (Data2VecAudio model)
- hubert — HubertForCTC (Hubert model)
- mctct — MCTCTForCTC (M-CTC-T model)
- sew — SEWForCTC (SEW model)
- sew-d — SEWDForCTC (SEW-D model)
- unispeech — UniSpeechForCTC (UniSpeech model)
- unispeech-sat — UniSpeechSatForCTC (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForCTC (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2BertForCTC (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2ConformerForCTC (Wav2Vec2-Conformer model)
- wavlm — WavLMForCTC (WavLM model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForCTC

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForCTC.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForCTC.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForCTC.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForSpeechSeq2Seq

###
class transformers.AutoModelForSpeechSeq2Seq

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Pop2PianoConfig configuration class: Pop2PianoForConditionalGeneration (Pop2Piano model)
  - SeamlessM4TConfig configuration class: SeamlessM4TForSpeechToText (SeamlessM4T model)
  - SeamlessM4Tv2Config configuration class: SeamlessM4Tv2ForSpeechToText (SeamlessM4Tv2 model)
  - Speech2TextConfig configuration class: Speech2TextForConditionalGeneration (Speech2Text model)
  - SpeechEncoderDecoderConfig configuration class: SpeechEncoderDecoderModel (Speech Encoder decoder model)
  - SpeechT5Config configuration class: SpeechT5ForSpeechToText (SpeechT5 model)
  - WhisperConfig configuration class: WhisperForConditionalGeneration (Whisper model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForSpeechSeq2Seq.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- pop2piano — Pop2PianoForConditionalGeneration (Pop2Piano model)
- seamless_m4t — SeamlessM4TForSpeechToText (SeamlessM4T model)
- seamless_m4t_v2 — SeamlessM4Tv2ForSpeechToText (SeamlessM4Tv2 model)
- speech-encoder-decoder — SpeechEncoderDecoderModel (Speech Encoder decoder model)
- speech_to_text — Speech2TextForConditionalGeneration (Speech2Text model)
- speecht5 — SpeechT5ForSpeechToText (SpeechT5 model)
- whisper — WhisperForConditionalGeneration (Whisper model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForSpeechSeq2Seq

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForSpeechSeq2Seq.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForSpeechSeq2Seq

###
class transformers.TFAutoModelForSpeechSeq2Seq

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Speech2TextConfig configuration class: TFSpeech2TextForConditionalGeneration (Speech2Text model)
  - WhisperConfig configuration class: TFWhisperForConditionalGeneration (Whisper model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForSpeechSeq2Seq.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- speech_to_text — TFSpeech2TextForConditionalGeneration (Speech2Text model)
- whisper — TFWhisperForConditionalGeneration (Whisper model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForSpeechSeq2Seq

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForSpeechSeq2Seq

###
class transformers.FlaxAutoModelForSpeechSeq2Seq

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - SpeechEncoderDecoderConfig configuration class: FlaxSpeechEncoderDecoderModel (Speech Encoder decoder model)
  - WhisperConfig configuration class: FlaxWhisperForConditionalGeneration (Whisper model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForSpeechSeq2Seq

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- speech-encoder-decoder — FlaxSpeechEncoderDecoderModel (Speech Encoder decoder model)
- whisper — FlaxWhisperForConditionalGeneration (Whisper model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForSpeechSeq2Seq

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForAudioXVector

###
class transformers.AutoModelForAudioXVector

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Data2VecAudioConfig configuration class: Data2VecAudioForXVector (Data2VecAudio model)
  - UniSpeechSatConfig configuration class: UniSpeechSatForXVector (UniSpeechSat model)
  - Wav2Vec2BertConfig configuration class: Wav2Vec2BertForXVector (Wav2Vec2-BERT model)
  - Wav2Vec2Config configuration class: Wav2Vec2ForXVector (Wav2Vec2 model)
  - Wav2Vec2ConformerConfig configuration class: Wav2Vec2ConformerForXVector (Wav2Vec2-Conformer model)
  - WavLMConfig configuration class: WavLMForXVector (WavLM model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForAudioXVector

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForAudioXVector.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- data2vec-audio — Data2VecAudioForXVector (Data2VecAudio model)
- unispeech-sat — UniSpeechSatForXVector (UniSpeechSat model)
- wav2vec2 — Wav2Vec2ForXVector (Wav2Vec2 model)
- wav2vec2-bert — Wav2Vec2BertForXVector (Wav2Vec2-BERT model)
- wav2vec2-conformer — Wav2Vec2ConformerForXVector (Wav2Vec2-Conformer model)
- wavlm — WavLMForXVector (WavLM model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForAudioXVector

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForAudioXVector.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForAudioXVector.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForAudioXVector.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForTextToSpectrogram

###
class transformers.AutoModelForTextToSpectrogram

< source >

( *args **kwargs )

###
AutoModelForTextToWaveform

###
class transformers.AutoModelForTextToWaveform

< source >

( *args **kwargs )

##
Multimodal

The following auto classes are available for the following multimodal tasks.

###
AutoModelForTableQuestionAnswering

###
class transformers.AutoModelForTableQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - TapasConfig configuration class: TapasForQuestionAnswering (TAPAS model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a table question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForTableQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
    >>> model = AutoModelForTableQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- tapas — TapasForQuestionAnswering (TAPAS model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForTableQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

    >>> # Update configuration during loading
    >>> model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/tapas_tf_model_config.json")
    >>> model = AutoModelForTableQuestionAnswering.from_pretrained(
    ...     "./tf_model/tapas_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForTableQuestionAnswering

###
class transformers.TFAutoModelForTableQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - TapasConfig configuration class: TFTapasForQuestionAnswering (TAPAS model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a table question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google/tapas-base-finetuned-wtq")
    >>> model = TFAutoModelForTableQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- tapas — TFTapasForQuestionAnswering (TAPAS model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForTableQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForTableQuestionAnswering.from_pretrained("google/tapas-base-finetuned-wtq", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/tapas_pt_model_config.json")
    >>> model = TFAutoModelForTableQuestionAnswering.from_pretrained(
    ...     "./pt_model/tapas_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForDocumentQuestionAnswering

###
class transformers.AutoModelForDocumentQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - LayoutLMConfig configuration class: LayoutLMForQuestionAnswering (LayoutLM model)
  - LayoutLMv2Config configuration class: LayoutLMv2ForQuestionAnswering (LayoutLMv2 model)
  - LayoutLMv3Config configuration class: LayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a document question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
    >>> model = AutoModelForDocumentQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- layoutlm — LayoutLMForQuestionAnswering (LayoutLM model)
- layoutlmv2 — LayoutLMv2ForQuestionAnswering (LayoutLMv2 model)
- layoutlmv3 — LayoutLMv3ForQuestionAnswering (LayoutLMv3 model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForDocumentQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

    >>> # Update configuration during loading
    >>> model = AutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/layoutlm_tf_model_config.json")
    >>> model = AutoModelForDocumentQuestionAnswering.from_pretrained(
    ...     "./tf_model/layoutlm_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForDocumentQuestionAnswering

###
class transformers.TFAutoModelForDocumentQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - LayoutLMConfig configuration class: TFLayoutLMForQuestionAnswering (LayoutLM model)
  - LayoutLMv3Config configuration class: TFLayoutLMv3ForQuestionAnswering (LayoutLMv3 model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a document question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")
    >>> model = TFAutoModelForDocumentQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- layoutlm — TFLayoutLMForQuestionAnswering (LayoutLM model)
- layoutlmv3 — TFLayoutLMv3ForQuestionAnswering (LayoutLMv3 model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForDocumentQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForDocumentQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="52e01b3", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/layoutlm_pt_model_config.json")
    >>> model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
    ...     "./pt_model/layoutlm_pytorch_model.bin", from_pt=True, config=config
    ... )

###
AutoModelForVisualQuestionAnswering

###
class transformers.AutoModelForVisualQuestionAnswering

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Blip2Config configuration class: Blip2ForConditionalGeneration (BLIP-2 model)
  - ViltConfig configuration class: ViltForQuestionAnswering (ViLT model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a visual question answering head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
    >>> model = AutoModelForVisualQuestionAnswering.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- blip-2 — Blip2ForConditionalGeneration (BLIP-2 model)
- vilt — ViltForQuestionAnswering (ViLT model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForVisualQuestionAnswering

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

    >>> # Update configuration during loading
    >>> model = AutoModelForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/vilt_tf_model_config.json")
    >>> model = AutoModelForVisualQuestionAnswering.from_pretrained(
    ...     "./tf_model/vilt_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
AutoModelForVision2Seq

###
class transformers.AutoModelForVision2Seq

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - Blip2Config configuration class: Blip2ForConditionalGeneration (BLIP-2 model)
  - BlipConfig configuration class: BlipForConditionalGeneration (BLIP model)
  - GitConfig configuration class: GitForCausalLM (GIT model)
  - InstructBlipConfig configuration class: InstructBlipForConditionalGeneration (InstructBLIP model)
  - Kosmos2Config configuration class: Kosmos2ForConditionalGeneration (KOSMOS-2 model)
  - LlavaConfig configuration class: LlavaForConditionalGeneration (LLaVa model)
  - Pix2StructConfig configuration class: Pix2StructForConditionalGeneration (Pix2Struct model)
  - VipLlavaConfig configuration class: VipLlavaForConditionalGeneration (VipLlava model)
  - VisionEncoderDecoderConfig configuration class: VisionEncoderDecoderModel (Vision Encoder decoder model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, AutoModelForVision2Seq

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = AutoModelForVision2Seq.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a tensorflow index checkpoint file (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- state_dict (Dict[str, torch.Tensor], optional) — A state dictionary to use instead of a state dictionary loaded from saved weights file.

  This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using save_pretrained() and from_pretrained() is not a simpler option.

- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_tf (`bool`, optional, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- blip — BlipForConditionalGeneration (BLIP model)
- blip-2 — Blip2ForConditionalGeneration (BLIP-2 model)
- git — GitForCausalLM (GIT model)
- instructblip — InstructBlipForConditionalGeneration (InstructBLIP model)
- kosmos-2 — Kosmos2ForConditionalGeneration (KOSMOS-2 model)
- llava — LlavaForConditionalGeneration (LLaVa model)
- pix2struct — Pix2StructForConditionalGeneration (Pix2Struct model)
- vipllava — VipLlavaForConditionalGeneration (VipLlava model)
- vision-encoder-decoder — VisionEncoderDecoderModel (Vision Encoder decoder model)

The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()`

Examples:

    >>> from transformers import AutoConfig, AutoModelForVision2Seq

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = AutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = AutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
    >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json")
    >>> model = AutoModelForVision2Seq.from_pretrained(
    ...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
    ... )

###
TFAutoModelForVision2Seq

###
class transformers.TFAutoModelForVision2Seq

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - BlipConfig configuration class: TFBlipForConditionalGeneration (BLIP model)
  - VisionEncoderDecoderConfig configuration class: TFVisionEncoderDecoderModel (Vision Encoder decoder model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForVision2Seq

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = TFAutoModelForVision2Seq.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- blip — TFBlipForConditionalGeneration (BLIP model)
- vision-encoder-decoder — TFVisionEncoderDecoderModel (Vision Encoder decoder model)

Examples:

    >>> from transformers import AutoConfig, TFAutoModelForVision2Seq

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = TFAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = TFAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = TFAutoModelForVision2Seq.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )

###
FlaxAutoModelForVision2Seq

###
class transformers.FlaxAutoModelForVision2Seq

< source >

( *args **kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created with the from_pretrained() class method or the from_config() class method.

This class cannot be instantiated directly using `__init__()` (throws an error).

####
from_config

< source >

( **kwargs )

Parameters

- config (PretrainedConfig) — The model class to instantiate is selected based on the configuration class:
  - VisionEncoderDecoderConfig configuration class: FlaxVisionEncoderDecoderModel (Vision Encoder decoder model)
- attn_implementation (`str`, optional) — The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using `F.scaled_dot_product_attention`), or `"flash_attention_2"` (using Dao-AILab/flash-attention). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.

Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.

Note: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use from_pretrained() to load the model weights.

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForVision2Seq

    >>> # Download configuration from huggingface.co and cache.
    >>> config = AutoConfig.from_pretrained("google-bert/bert-base-cased")
    >>> model = FlaxAutoModelForVision2Seq.from_config(config)

####
from_pretrained

< source >

( *model_args **kwargs )

Parameters

- pretrained_model_name_or_path (`str` or `os.PathLike`) — Can be either:
  - A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
  - A path to a directory containing model weights saved using save_pretrained(), e.g., `./my_model_directory/`.
  - A path or url to a PyTorch state_dict save file (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
- model_args (additional positional arguments, optional) — Will be passed along to the underlying model `__init__()` method.
- config (PretrainedConfig, optional) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when:
  - The model is a model provided by the library (loaded with the model id string of a pretrained model).
  - The model was saved using save_pretrained() and is reloaded by supplying the save directory.
  - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named config.json is found in the directory.
- cache_dir (`str` or `os.PathLike`, optional) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used.
- from_pt (`bool`, optional, defaults to `False`) — Load the model weights from a PyTorch checkpoint save file (see docstring of `pretrained_model_name_or_path` argument).
- force_download (`bool`, optional, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist.
- resume_download (`bool`, optional, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists.
- proxies (`Dict[str, str]`, optional) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- output_loading_info(`bool`, optional, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
- local_files_only(`bool`, optional, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model).
- revision (`str`, optional, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- trust_remote_code (`bool`, optional, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
- code_revision (`str`, optional, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git.
- kwargs (additional keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded:
  - If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done)
  - If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function (from_pretrained()). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function.

Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.

The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:

- vision-encoder-decoder — FlaxVisionEncoderDecoderModel (Vision Encoder decoder model)

Examples:

    >>> from transformers import AutoConfig, FlaxAutoModelForVision2Seq

    >>> # Download model and configuration from huggingface.co and cache.
    >>> model = FlaxAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased")

    >>> # Update configuration during loading
    >>> model = FlaxAutoModelForVision2Seq.from_pretrained("google-bert/bert-base-cased", output_attentions=True)
    >>> model.config.output_attentions
    True

    >>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
    >>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
    >>> model = FlaxAutoModelForVision2Seq.from_pretrained(
    ...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
    ... )