#
DeBERTa-v2

##
Overview

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019.

It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in RoBERTa.

The abstract from the paper is the following:

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.

The following information is visible directly on the original implementation repository. DeBERTa v2 is the second version of the DeBERTa model. It includes the 1.5B model used for the SuperGLUE single-model submission and achieving 89.9, versus human baseline 89.8. You can find more details about this submission in the authors’ blog

New in v2:

- Vocabulary In v2 the tokenizer is changed to use a new vocabulary of size 128K built from the training data. Instead of a GPT2-based tokenizer, the tokenizer is now sentencepiece-based tokenizer.
- nGiE(nGram Induced Input Encoding) The DeBERTa-v2 model uses an additional convolution layer aside with the first transformer layer to better learn the local dependency of input tokens.
- Sharing position projection matrix with content projection matrix in attention layer Based on previous experiments, this can save parameters without affecting the performance.
- Apply bucket to encode relative positions The DeBERTa-v2 model uses log bucket to encode relative positions similar to T5.
- 900M model & 1.5B model Two additional model sizes are available: 900M and 1.5B, which significantly improves the performance of downstream tasks.

This model was contributed by DeBERTa. This model TF 2.0 implementation was contributed by kamalkraj. The original code can be found here.

##
Resources

- Text classification task guide
- Token classification task guide
- Question answering task guide
- Masked language modeling task guide
- Multiple choice task guide

##
DebertaV2Config

###
class transformers.DebertaV2Config

< source >

( vocab_size = 128100 hidden_size = 1536 num_hidden_layers = 24 num_attention_heads = 24 intermediate_size = 6144 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 0 initializer_range = 0.02 layer_norm_eps = 1e-07 relative_attention = False max_relative_positions = -1 pad_token_id = 0 position_biased_input = True pos_att_type = None pooler_dropout = 0 pooler_hidden_act = 'gelu' **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 128100) — Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling DebertaV2Model.
- hidden_size (`int`, optional, defaults to 1536) — Dimensionality of the encoder layers and the pooler layer.
- num_hidden_layers (`int`, optional, defaults to 24) — Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, optional, defaults to 24) — Number of attention heads for each attention layer in the Transformer encoder.
- intermediate_size (`int`, optional, defaults to 6144) — Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.
- hidden_act (`str` or `Callable`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` and `"gelu_new"` are supported.
- hidden_dropout_prob (`float`, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`float`, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.
- max_position_embeddings (`int`, optional, defaults to 512) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
- type_vocab_size (`int`, optional, defaults to 0) — The vocabulary size of the `token_type_ids` passed when calling DebertaModel or TFDebertaModel.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- layer_norm_eps (`float`, optional, defaults to 1e-7) — The epsilon used by the layer normalization layers.
- relative_attention (`bool`, optional, defaults to `True`) — Whether use relative position encoding.
- max_relative_positions (`int`, optional, defaults to -1) — The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value as `max_position_embeddings`.
- pad_token_id (`int`, optional, defaults to 0) — The value used to pad input_ids.
- position_biased_input (`bool`, optional, defaults to `False`) — Whether add absolute position embedding to content embedding.
- pos_att_type (`List[str]`, optional) — The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`, `["p2c", "c2p"]`, `["p2c", "c2p"]`.
- layer_norm_eps (`float`, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.

This is the configuration class to store the configuration of a DebertaV2Model. It is used to instantiate a DeBERTa-v2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DeBERTa microsoft/deberta-v2-xlarge architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> from transformers import DebertaV2Config, DebertaV2Model

    >>> # Initializing a DeBERTa-v2 microsoft/deberta-v2-xlarge style configuration
    >>> configuration = DebertaV2Config()

    >>> # Initializing a model (with random weights) from the microsoft/deberta-v2-xlarge style configuration
    >>> model = DebertaV2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

##
DebertaV2Tokenizer

###
class transformers.DebertaV2Tokenizer

< source >

( vocab_file do_lower_case = False split_by_punct = False bos_token = '[CLS]' eos_token = '[SEP]' unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' sp_model_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None **kwargs )

Parameters

- vocab_file (`str`) — SentencePiece file (generally has a .spm extension) that contains the vocabulary necessary to instantiate a tokenizer.
- do_lower_case (`bool`, optional, defaults to `False`) — Whether or not to lowercase the input when tokenizing.
- bos_token (`string`, optional, defaults to `"[CLS]"`) — The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`.
- eos_token (`string`, optional, defaults to `"[SEP]"`) — The end of sequence token. When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`.
- unk_token (`str`, optional, defaults to `"[UNK]"`) — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
- sep_token (`str`, optional, defaults to `"[SEP]"`) — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
- pad_token (`str`, optional, defaults to `"[PAD]"`) — The token used for padding, for example when batching sequences of different lengths.
- cls_token (`str`, optional, defaults to `"[CLS]"`) — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
- mask_token (`str`, optional, defaults to `"[MASK]"`) — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
- sp_model_kwargs (`dict`, optional) — Will be passed to the `SentencePieceProcessor.__init__()` method. The Python wrapper for SentencePiece can be used, among other things, to set:

  - `enable_sampling`: Enable subword regularization.

  - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

    - `nbest_size = {0,1}`: No sampling is performed.
    - `nbest_size > 1`: samples from the nbest_size results.
    - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.

  - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.

Constructs a DeBERTa-v2 tokenizer. Based on SentencePiece.

####
build_inputs_with_special_tokens

< source >

( token_ids_0 token_ids_1 = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs to which the special tokens will be added.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.

Returns

`List[int]`

List of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A DeBERTa sequence has the following format:

- single sequence: [CLS] X [SEP]
- pair of sequences: [CLS] A [SEP] B [SEP]

####
get_special_tokens_mask

< source >

( token_ids_0 token_ids_1 = None already_has_special_tokens = False ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.
- already_has_special_tokens (`bool`, optional, defaults to `False`) — Whether or not the token list is already formatted with special tokens for the model.

Returns

`List[int]`

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.

####
create_token_type_ids_from_sequences

< source >

( token_ids_0 token_ids_1 = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.

Returns

`List[int]`

List of token type IDs according to the given sequence(s).

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa

sequence pair mask has the following format:

    0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
    | first sequence    | second sequence |

If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

####
save_vocabulary

< source >

( save_directory: str filename_prefix: typing.Optional[str] = None )

##
DebertaV2TokenizerFast

###
class transformers.DebertaV2TokenizerFast

< source >

( vocab_file = None tokenizer_file = None do_lower_case = False split_by_punct = False bos_token = '[CLS]' eos_token = '[SEP]' unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' **kwargs )

Parameters

- vocab_file (`str`) — SentencePiece file (generally has a .spm extension) that contains the vocabulary necessary to instantiate a tokenizer.
- do_lower_case (`bool`, optional, defaults to `False`) — Whether or not to lowercase the input when tokenizing.
- bos_token (`string`, optional, defaults to `"[CLS]"`) — The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`.
- eos_token (`string`, optional, defaults to `"[SEP]"`) — The end of sequence token. When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`.
- unk_token (`str`, optional, defaults to `"[UNK]"`) — The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.
- sep_token (`str`, optional, defaults to `"[SEP]"`) — The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.
- pad_token (`str`, optional, defaults to `"[PAD]"`) — The token used for padding, for example when batching sequences of different lengths.
- cls_token (`str`, optional, defaults to `"[CLS]"`) — The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.
- mask_token (`str`, optional, defaults to `"[MASK]"`) — The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
- sp_model_kwargs (`dict`, optional) — Will be passed to the `SentencePieceProcessor.__init__()` method. The Python wrapper for SentencePiece can be used, among other things, to set:

  - `enable_sampling`: Enable subword regularization.

  - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

    - `nbest_size = {0,1}`: No sampling is performed.
    - `nbest_size > 1`: samples from the nbest_size results.
    - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.

  - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.

Constructs a DeBERTa-v2 fast tokenizer. Based on SentencePiece.

####
build_inputs_with_special_tokens

< source >

( token_ids_0 token_ids_1 = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs to which the special tokens will be added.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.

Returns

`List[int]`

List of input IDs with the appropriate special tokens.

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A DeBERTa sequence has the following format:

- single sequence: [CLS] X [SEP]
- pair of sequences: [CLS] A [SEP] B [SEP]

####
create_token_type_ids_from_sequences

< source >

( token_ids_0 token_ids_1 = None ) →
`List[int]`

Parameters

- token_ids_0 (`List[int]`) — List of IDs.
- token_ids_1 (`List[int]`, optional) — Optional second list of IDs for sequence pairs.

Returns

`List[int]`

List of token type IDs according to the given sequence(s).

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa

sequence pair mask has the following format:

    0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
    | first sequence    | second sequence |

If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).

Pytorch
Hide Pytorch content

##
DebertaV2Model

###
class transformers.DebertaV2Model

< source >

( config )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top. The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.BaseModelOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The DebertaV2Model forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, DebertaV2Model
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
    >>> model = DebertaV2Model.from_pretrained("microsoft/deberta-v2-xlarge")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
    >>> outputs = model(**inputs)

    >>> last_hidden_states = outputs.last_hidden_state

##
DebertaV2PreTrainedModel

###
class transformers.DebertaV2PreTrainedModel

< source >

( config: PretrainedConfig *inputs **kwargs )

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.

####
_forward_unimplemented

< source >

( *input: typing.Any )

Defines the computation performed at every call.

Should be overridden by all subclasses.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

##
DebertaV2ForMaskedLM

###
class transformers.DebertaV2ForMaskedLM

< source >

( config )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a `language modeling` head on top. The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.MaskedLMOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`

Returns

transformers.modeling_outputs.MaskedLMOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.MaskedLMOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Masked language modeling (MLM) loss.

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The DebertaV2ForMaskedLM forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, DebertaV2ForMaskedLM
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
    >>> model = DebertaV2ForMaskedLM.from_pretrained("microsoft/deberta-v2-xlarge")

    >>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> # retrieve index of [MASK]
    >>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]

    >>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)

    >>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
    >>> # mask labels of non-[MASK] tokens
    >>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

    >>> outputs = model(**inputs, labels=labels)

##
DebertaV2ForSequenceClassification

###
class transformers.DebertaV2ForSequenceClassification

< source >

( config )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.SequenceClassifierOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size,)`, optional) — Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

Returns

transformers.modeling_outputs.SequenceClassifierOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Classification (or regression if config.num_labels==1) loss.

- logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The DebertaV2ForSequenceClassification forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example of single-label classification:

    >>> import torch
    >>> from transformers import AutoTokenizer, DebertaV2ForSequenceClassification

    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
    >>> model = DebertaV2ForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_class_id = logits.argmax().item()

    >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
    >>> num_labels = len(model.config.id2label)
    >>> model = DebertaV2ForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge", num_labels=num_labels)

    >>> labels = torch.tensor([1])
    >>> loss = model(**inputs, labels=labels).loss

Example of multi-label classification:

    >>> import torch
    >>> from transformers import AutoTokenizer, DebertaV2ForSequenceClassification

    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
    >>> model = DebertaV2ForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge", problem_type="multi_label_classification")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

    >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
    >>> num_labels = len(model.config.id2label)
    >>> model = DebertaV2ForSequenceClassification.from_pretrained(
    ...     "microsoft/deberta-v2-xlarge", num_labels=num_labels, problem_type="multi_label_classification"
    ... )

    >>> labels = torch.sum(
    ...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
    ... ).to(torch.float)
    >>> loss = model(**inputs, labels=labels).loss

##
DebertaV2ForTokenClassification

###
class transformers.DebertaV2ForTokenClassification

< source >

( config )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.TokenClassifierOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

Returns

transformers.modeling_outputs.TokenClassifierOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.TokenClassifierOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Classification loss.

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) — Classification scores (before SoftMax).

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The DebertaV2ForTokenClassification forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, DebertaV2ForTokenClassification
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
    >>> model = DebertaV2ForTokenClassification.from_pretrained("microsoft/deberta-v2-xlarge")

    >>> inputs = tokenizer(
    ...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
    ... )

    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits

    >>> predicted_token_class_ids = logits.argmax(-1)

    >>> # Note that tokens are classified rather then input words which means that
    >>> # there might be more predicted token classes than words.
    >>> # Multiple token classes might account for the same word
    >>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]

    >>> labels = predicted_token_class_ids
    >>> loss = model(**inputs, labels=labels).loss

##
DebertaV2ForQuestionAnswering

###
class transformers.DebertaV2ForQuestionAnswering

< source >

( config )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None start_positions: typing.Optional[torch.Tensor] = None end_positions: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.QuestionAnsweringModelOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- start_positions (`torch.LongTensor` of shape `(batch_size,)`, optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss.
- end_positions (`torch.LongTensor` of shape `(batch_size,)`, optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss.

Returns

transformers.modeling_outputs.QuestionAnsweringModelOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

- start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) — Span-start scores (before SoftMax).

- end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) — Span-end scores (before SoftMax).

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The DebertaV2ForQuestionAnswering forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, DebertaV2ForQuestionAnswering
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
    >>> model = DebertaV2ForQuestionAnswering.from_pretrained("microsoft/deberta-v2-xlarge")

    >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

    >>> inputs = tokenizer(question, text, return_tensors="pt")
    >>> with torch.no_grad():
    ...     outputs = model(**inputs)

    >>> answer_start_index = outputs.start_logits.argmax()
    >>> answer_end_index = outputs.end_logits.argmax()

    >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]

    >>> # target is "nice puppet"
    >>> target_start_index = torch.tensor([2])
    >>> target_end_index = torch.tensor([9])

    >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
    >>> loss = outputs.loss

##
DebertaV2ForMultipleChoice

###
class transformers.DebertaV2ForMultipleChoice

< source >

( config )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.MultipleChoiceModelOutput or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- labels (`torch.LongTensor` of shape `(batch_size,)`, optional) — Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)

Returns

transformers.modeling_outputs.MultipleChoiceModelOutput or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.MultipleChoiceModelOutput or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`torch.FloatTensor` of shape (1,), optional, returned when `labels` is provided) — Classification loss.

- logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`) — num_choices is the second dimension of the input tensors. (see input_ids above).

  Classification scores (before SoftMax).

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The DebertaV2ForMultipleChoice forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, DebertaV2ForMultipleChoice
    >>> import torch

    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge")
    >>> model = DebertaV2ForMultipleChoice.from_pretrained("microsoft/deberta-v2-xlarge")

    >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
    >>> choice0 = "It is eaten with a fork and a knife."
    >>> choice1 = "It is eaten while held in the hand."
    >>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

    >>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
    >>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1

    >>> # the linear classifier still needs to be trained
    >>> loss = outputs.loss
    >>> logits = outputs.logits

TensorFlow
Hide TensorFlow content

##
TFDebertaV2Model

###
class transformers.TFDebertaV2Model

< source >

( *args **kwargs )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top. The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: Optional[bool] = False ) →
transformers.modeling_tf_outputs.TFBaseModelOutput or `tuple(tf.Tensor)`

Parameters

- input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a [`~utils.ModelOutput“] instead of a plain tuple.

Returns

transformers.modeling_tf_outputs.TFBaseModelOutput or `tuple(tf.Tensor)`

A transformers.modeling_tf_outputs.TFBaseModelOutput or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- hidden_states (`tuple(tf.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFDebertaV2Model forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, TFDebertaV2Model
    >>> import tensorflow as tf

    >>> tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-v2-xlarge")
    >>> model = TFDebertaV2Model.from_pretrained("kamalkraj/deberta-v2-xlarge")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
    >>> outputs = model(inputs)

    >>> last_hidden_states = outputs.last_hidden_state

##
TFDebertaV2PreTrainedModel

###
class transformers.TFDebertaV2PreTrainedModel

< source >

( *args **kwargs )

An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.

####
call

< source >

( inputs training = None mask = None )

Calls the model on new inputs and returns the outputs as tensors.

In this case `call()` just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Note: This method should not be called directly. It is only meant to be overridden when subclassing `tf.keras.Model`. To call a model on an input, always use the `__call__()` method, i.e. `model(inputs)`, which relies on the underlying `call()` method.

##
TFDebertaV2ForMaskedLM

###
class transformers.TFDebertaV2ForMaskedLM

< source >

( *args **kwargs )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a `language modeling` head on top. The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) →
transformers.modeling_tf_outputs.TFMaskedLMOutput or `tuple(tf.Tensor)`

Parameters

- input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a [`~utils.ModelOutput“] instead of a plain tuple.
- labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`

Returns

transformers.modeling_tf_outputs.TFMaskedLMOutput or `tuple(tf.Tensor)`

A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`tf.Tensor` of shape `(n,)`, optional, where n is the number of non-masked labels, returned when `labels` is provided) — Masked language modeling (MLM) loss.

- logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

- hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFDebertaV2ForMaskedLM forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, TFDebertaV2ForMaskedLM
    >>> import tensorflow as tf

    >>> tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-v2-xlarge")
    >>> model = TFDebertaV2ForMaskedLM.from_pretrained("kamalkraj/deberta-v2-xlarge")

    >>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="tf")
    >>> logits = model(**inputs).logits

    >>> # retrieve index of [MASK]
    >>> mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
    >>> selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)

    >>> predicted_token_id = tf.math.argmax(selected_logits, axis=-1)

    >>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
    >>> # mask labels of non-[MASK] tokens
    >>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

    >>> outputs = model(**inputs, labels=labels)

##
TFDebertaV2ForSequenceClassification

###
class transformers.TFDebertaV2ForSequenceClassification

< source >

( *args **kwargs )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) →
transformers.modeling_tf_outputs.TFSequenceClassifierOutput or `tuple(tf.Tensor)`

Parameters

- input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a [`~utils.ModelOutput“] instead of a plain tuple.
- labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, optional) — Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

Returns

transformers.modeling_tf_outputs.TFSequenceClassifierOutput or `tuple(tf.Tensor)`

A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`tf.Tensor` of shape `(batch_size, )`, optional, returned when `labels` is provided) — Classification (or regression if config.num_labels==1) loss.

- logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`) — Classification (or regression if config.num_labels==1) scores (before SoftMax).

- hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFDebertaV2ForSequenceClassification forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, TFDebertaV2ForSequenceClassification
    >>> import tensorflow as tf

    >>> tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-v2-xlarge")
    >>> model = TFDebertaV2ForSequenceClassification.from_pretrained("kamalkraj/deberta-v2-xlarge")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")

    >>> logits = model(**inputs).logits

    >>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])

    >>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
    >>> num_labels = len(model.config.id2label)
    >>> model = TFDebertaV2ForSequenceClassification.from_pretrained("kamalkraj/deberta-v2-xlarge", num_labels=num_labels)

    >>> labels = tf.constant(1)
    >>> loss = model(**inputs, labels=labels).loss

##
TFDebertaV2ForTokenClassification

###
class transformers.TFDebertaV2ForTokenClassification

< source >

( *args **kwargs )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) →
transformers.modeling_tf_outputs.TFTokenClassifierOutput or `tuple(tf.Tensor)`

Parameters

- input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a [`~utils.ModelOutput“] instead of a plain tuple.
- labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

Returns

transformers.modeling_tf_outputs.TFTokenClassifierOutput or `tuple(tf.Tensor)`

A transformers.modeling_tf_outputs.TFTokenClassifierOutput or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`tf.Tensor` of shape `(n,)`, optional, where n is the number of unmasked labels, returned when `labels` is provided) — Classification loss.

- logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`) — Classification scores (before SoftMax).

- hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFDebertaV2ForTokenClassification forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, TFDebertaV2ForTokenClassification
    >>> import tensorflow as tf

    >>> tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-v2-xlarge")
    >>> model = TFDebertaV2ForTokenClassification.from_pretrained("kamalkraj/deberta-v2-xlarge")

    >>> inputs = tokenizer(
    ...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
    ... )

    >>> logits = model(**inputs).logits
    >>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)

    >>> # Note that tokens are classified rather then input words which means that
    >>> # there might be more predicted token classes than words.
    >>> # Multiple token classes might account for the same word
    >>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]

    >>> labels = predicted_token_class_ids
    >>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)

##
TFDebertaV2ForQuestionAnswering

###
class transformers.TFDebertaV2ForQuestionAnswering

< source >

( *args **kwargs )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None start_positions: np.ndarray | tf.Tensor | None = None end_positions: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) →
transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or `tuple(tf.Tensor)`

Parameters

- input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a [`~utils.ModelOutput“] instead of a plain tuple.
- start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss.
- end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss.

Returns

transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or `tuple(tf.Tensor)`

A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`tf.Tensor` of shape `(batch_size, )`, optional, returned when `start_positions` and `end_positions` are provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

- start_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Span-start scores (before SoftMax).

- end_logits (`tf.Tensor` of shape `(batch_size, sequence_length)`) — Span-end scores (before SoftMax).

- hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFDebertaV2ForQuestionAnswering forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, TFDebertaV2ForQuestionAnswering
    >>> import tensorflow as tf

    >>> tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-v2-xlarge")
    >>> model = TFDebertaV2ForQuestionAnswering.from_pretrained("kamalkraj/deberta-v2-xlarge")

    >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

    >>> inputs = tokenizer(question, text, return_tensors="tf")
    >>> outputs = model(**inputs)

    >>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
    >>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

    >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]

    >>> # target is "nice puppet"
    >>> target_start_index = tf.constant([14])
    >>> target_end_index = tf.constant([15])

    >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
    >>> loss = tf.math.reduce_mean(outputs.loss)

##
TFDebertaV2ForMultipleChoice

###
class transformers.TFDebertaV2ForMultipleChoice

< source >

( *args **kwargs )

Parameters

- config (DebertaV2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

DeBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.

The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It’s build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with subclassing then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

####
call

< source >

( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) →
transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or `tuple(tf.Tensor)`

Parameters

- input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_choices, sequence_length)`) — Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- token_type_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  - 0 corresponds to a sentence A token,
  - 1 corresponds to a sentence B token.

  What are token type IDs?

- position_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`.

  What are position IDs?

- inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a [`~utils.ModelOutput“] instead of a plain tuple.
- labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, optional) — Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)

Returns

transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or `tuple(tf.Tensor)`

A transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or a tuple of `tf.Tensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (DebertaV2Config) and inputs.

- loss (`tf.Tensor` of shape (batch_size, ), optional, returned when `labels` is provided) — Classification loss.

- logits (`tf.Tensor` of shape `(batch_size, num_choices)`) — num_choices is the second dimension of the input tensors. (see input_ids above).

  Classification scores (before SoftMax).

- hidden_states (`tuple(tf.Tensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs.

- attentions (`tuple(tf.Tensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The TFDebertaV2ForMultipleChoice forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, TFDebertaV2ForMultipleChoice
    >>> import tensorflow as tf

    >>> tokenizer = AutoTokenizer.from_pretrained("kamalkraj/deberta-v2-xlarge")
    >>> model = TFDebertaV2ForMultipleChoice.from_pretrained("kamalkraj/deberta-v2-xlarge")

    >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
    >>> choice0 = "It is eaten with a fork and a knife."
    >>> choice1 = "It is eaten while held in the hand."

    >>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
    >>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
    >>> outputs = model(inputs)  # batch size is 1

    >>> # the linear classifier still needs to be trained
    >>> logits = outputs.logits