#
Data Collator

Data collators是一个对象，通过使用数据集元素列表作为输入来形成一个批次。这些元素与 `train_dataset` 或 `eval_dataset` 的元素类型相同。

为了能够构建批次，Data collators可能会应用一些预处理（比如填充）。其中一些（比如DataCollatorForLanguageModeling）还会在形成的批次上应用一些随机数据增强（比如随机掩码）。

在示例脚本或示例notebooks中可以找到使用的示例。

##
Default data collator

####
transformers.default_data_collator

< source >

( features: List return_tensors = 'pt' )

Very simple data collator that simply collates batches of dict-like objects and performs special handling for potential keys named:

- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per object

Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs to the model. See glue and ner for example of how it’s useful.

##
DefaultDataCollator

###
class transformers.DefaultDataCollator

< source >

( return_tensors: str = 'pt' )

Parameters

- return_tensors (`str`, optional, defaults to `"pt"`) — The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.

Very simple data collator that simply collates batches of dict-like objects and performs special handling for potential keys named:

- `label`: handles a single value (int or float) per object
- `label_ids`: handles a list of values per object

Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs to the model. See glue and ner for example of how it’s useful.

This is an object (like other data collators) rather than a pure function like default_data_collator. This can be helpful if you need to set a return_tensors value at initialization.

##
DataCollatorWithPadding

###
class transformers.DataCollatorWithPadding

< source >

( tokenizer: PreTrainedTokenizerBase padding: Union = True max_length: Optional = None pad_to_multiple_of: Optional = None return_tensors: str = 'pt' )

Parameters

- tokenizer (PreTrainedTokenizer or PreTrainedTokenizerFast) — The tokenizer used for encoding the data.
- padding (`bool`, `str` or PaddingStrategy, optional, defaults to `True`) — Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among:
  - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single sequence is provided).
  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided.
  - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
- max_length (`int`, optional) — Maximum length of the returned list and optionally padding length (see above).
- pad_to_multiple_of (`int`, optional) — If set will pad the sequence to a multiple of the provided value.

  This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

- return_tensors (`str`, optional, defaults to `"pt"`) — The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.

Data collator that will dynamically pad the inputs received.

##
DataCollatorForTokenClassification

###
class transformers.DataCollatorForTokenClassification

< source >

( tokenizer: PreTrainedTokenizerBase padding: Union = True max_length: Optional = None pad_to_multiple_of: Optional = None label_pad_token_id: int = -100 return_tensors: str = 'pt' )

Parameters

- tokenizer (PreTrainedTokenizer or PreTrainedTokenizerFast) — The tokenizer used for encoding the data.
- padding (`bool`, `str` or PaddingStrategy, optional, defaults to `True`) — Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among:
  - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single sequence is provided).
  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided.
  - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
- max_length (`int`, optional) — Maximum length of the returned list and optionally padding length (see above).
- pad_to_multiple_of (`int`, optional) — If set will pad the sequence to a multiple of the provided value.

  This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

- label_pad_token_id (`int`, optional, defaults to -100) — The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
- return_tensors (`str`, optional, defaults to `"pt"`) — The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.

Data collator that will dynamically pad the inputs received, as well as the labels.

##
DataCollatorForSeq2Seq

###
class transformers.DataCollatorForSeq2Seq

< source >

( tokenizer: PreTrainedTokenizerBase model: Optional = None padding: Union = True max_length: Optional = None pad_to_multiple_of: Optional = None label_pad_token_id: int = -100 return_tensors: str = 'pt' )

Parameters

- tokenizer (PreTrainedTokenizer or PreTrainedTokenizerFast) — The tokenizer used for encoding the data.
- model (PreTrainedModel, optional) — The model that is being trained. If set and has the prepare_decoder_input_ids_from_labels, use it to prepare the decoder_input_ids

  This is useful when using label_smoothing to avoid calculating loss twice.

- padding (`bool`, `str` or PaddingStrategy, optional, defaults to `True`) — Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among:
  - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single sequence is provided).
  - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided.
  - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
- max_length (`int`, optional) — Maximum length of the returned list and optionally padding length (see above).
- pad_to_multiple_of (`int`, optional) — If set will pad the sequence to a multiple of the provided value.

  This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).

- label_pad_token_id (`int`, optional, defaults to -100) — The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
- return_tensors (`str`, optional, defaults to `"pt"`) — The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.

Data collator that will dynamically pad the inputs received, as well as the labels.

##
DataCollatorForLanguageModeling

###
class transformers.DataCollatorForLanguageModeling

< source >

( tokenizer: PreTrainedTokenizerBase mlm: bool = True mlm_probability: float = 0.15 pad_to_multiple_of: Optional = None tf_experimental_compile: bool = False return_tensors: str = 'pt' )

Parameters

- tokenizer (PreTrainedTokenizer or PreTrainedTokenizerFast) — The tokenizer used for encoding the data.
- mlm (`bool`, optional, defaults to `True`) — Whether or not to use masked language modeling. If set to `False`, the labels are the same as the inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for non-masked tokens and the value to predict for the masked token.
- mlm_probability (`float`, optional, defaults to 0.15) — The probability with which to (randomly) mask tokens in the input, when `mlm` is set to `True`.
- pad_to_multiple_of (`int`, optional) — If set will pad the sequence to a multiple of the provided value.
- return_tensors (`str`) — The type of Tensor to return. Allowable values are “np”, “pt” and “tf”.

Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same length.

For best performance, this data collator should be used with a dataset having items that are dictionaries or BatchEncoding, with the `"special_tokens_mask"` key, as returned by a PreTrainedTokenizer or a PreTrainedTokenizerFast with the argument `return_special_tokens_mask=True`.

####
numpy_mask_tokens

< source >

( inputs: Any special_tokens_mask: Optional = None )

Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.

####
tf_mask_tokens

< source >

( inputs: Any vocab_size mask_token_id special_tokens_mask: Optional = None )

Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.

####
torch_mask_tokens

< source >

( inputs: Any special_tokens_mask: Optional = None )

Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.

##
DataCollatorForWholeWordMask

###
class transformers.DataCollatorForWholeWordMask

< source >

( tokenizer: PreTrainedTokenizerBase mlm: bool = True mlm_probability: float = 0.15 pad_to_multiple_of: Optional = None tf_experimental_compile: bool = False return_tensors: str = 'pt' )

Data collator used for language modeling that masks entire words.

- collates batches of tensors, honoring their tokenizer’s pad_token
- preprocesses batches for masked language modeling

This collator relies on details of the implementation of subword tokenization by `BertTokenizer`, specifically that subword tokens are prefixed with ##. For tokenizers that do not adhere to this scheme, this collator will produce an output that is roughly equivalent to `.DataCollatorForLanguageModeling`.

####
numpy_mask_tokens

< source >

( inputs: Any mask_labels: Any )

Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set ‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.

####
tf_mask_tokens

< source >

( inputs: Any mask_labels: Any )

Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set ‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.

####
torch_mask_tokens

< source >

( inputs: Any mask_labels: Any )

Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set ‘mask_labels’ means we use whole word mask (wwm), we directly mask idxs according to it’s ref.

##
DataCollatorForPermutationLanguageModeling

###
class transformers.DataCollatorForPermutationLanguageModeling

< source >

( tokenizer: PreTrainedTokenizerBase plm_probability: float = 0.16666666666666666 max_span_length: int = 5 return_tensors: str = 'pt' )

Data collator used for permutation language modeling.

- collates batches of tensors, honoring their tokenizer’s pad_token
- preprocesses batches for permutation language modeling with procedures specific to XLNet

####
numpy_mask_tokens

< source >

( inputs: Any )

The masked tokens to be predicted for a particular sequence are determined by the following algorithm:

0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
1. Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
2. Reserve a context of length `context_length = span_length / plm_probability` to surround span to be masked
3. Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length - span_length]` and mask tokens `start_index:start_index + span_length`
4. Set `cur_len = cur_len + context_length`. If `cur_len < max_len` (i.e. there are tokens remaining in the sequence to be processed), repeat from Step 1.

####
tf_mask_tokens

< source >

( inputs: Any )

The masked tokens to be predicted for a particular sequence are determined by the following algorithm:

0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
1. Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
2. Reserve a context of length `context_length = span_length / plm_probability` to surround span to be masked
3. Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length - span_length]` and mask tokens `start_index:start_index + span_length`
4. Set `cur_len = cur_len + context_length`. If `cur_len < max_len` (i.e. there are tokens remaining in the sequence to be processed), repeat from Step 1.

####
torch_mask_tokens

< source >

( inputs: Any )

The masked tokens to be predicted for a particular sequence are determined by the following algorithm:

0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
1. Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
2. Reserve a context of length `context_length = span_length / plm_probability` to surround span to be masked
3. Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length - span_length]` and mask tokens `start_index:start_index + span_length`
4. Set `cur_len = cur_len + context_length`. If `cur_len < max_len` (i.e. there are tokens remaining in the sequence to be processed), repeat from Step 1.

< > Update on GitHub