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Data Collator

Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of the same type as the elements of train_dataset or eval_dataset.

To be able to build batches, data collators may apply some processing (like padding). Some of them (like [DataCollatorForLanguageModeling]) also apply some random data augmentation (like random masking) on the formed batch.

Examples of use can be found in the example scripts or example notebooks.

Default data collator

[[autodoc]] data.data_collator.default_data_collator

DefaultDataCollator

[[autodoc]] data.data_collator.DefaultDataCollator

DataCollatorWithPadding

[[autodoc]] data.data_collator.DataCollatorWithPadding

DataCollatorForTokenClassification

[[autodoc]] data.data_collator.DataCollatorForTokenClassification

DataCollatorForSeq2Seq

[[autodoc]] data.data_collator.DataCollatorForSeq2Seq

DataCollatorForLanguageModeling

[[autodoc]] data.data_collator.DataCollatorForLanguageModeling - numpy_mask_tokens - torch_mask_tokens

DataCollatorForWholeWordMask

[[autodoc]] data.data_collator.DataCollatorForWholeWordMask - numpy_mask_tokens - torch_mask_tokens

DataCollatorForPermutationLanguageModeling

[[autodoc]] data.data_collator.DataCollatorForPermutationLanguageModeling - numpy_mask_tokens - torch_mask_tokens

DataCollatorWithFlattening

[[autodoc]] data.data_collator.DataCollatorWithFlattening

DataCollatorForMultipleChoice

[[autodoc]] data.data_collator.DataCollatorForMultipleChoice