74 lines
2.3 KiB
Markdown
74 lines
2.3 KiB
Markdown
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# Data Collator
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Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
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the same type as the elements of `train_dataset` or `eval_dataset`.
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To be able to build batches, data collators may apply some processing (like padding). Some of them (like
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[`DataCollatorForLanguageModeling`]) also apply some random data augmentation (like random masking)
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on the formed batch.
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Examples of use can be found in the [example scripts](../examples) or [example notebooks](../notebooks).
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## Default data collator
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[[autodoc]] data.data_collator.default_data_collator
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## DefaultDataCollator
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[[autodoc]] data.data_collator.DefaultDataCollator
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## DataCollatorWithPadding
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[[autodoc]] data.data_collator.DataCollatorWithPadding
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## DataCollatorForTokenClassification
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[[autodoc]] data.data_collator.DataCollatorForTokenClassification
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## DataCollatorForSeq2Seq
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[[autodoc]] data.data_collator.DataCollatorForSeq2Seq
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## DataCollatorForLanguageModeling
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[[autodoc]] data.data_collator.DataCollatorForLanguageModeling
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- numpy_mask_tokens
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- tf_mask_tokens
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- torch_mask_tokens
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## DataCollatorForWholeWordMask
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[[autodoc]] data.data_collator.DataCollatorForWholeWordMask
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- numpy_mask_tokens
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- tf_mask_tokens
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- torch_mask_tokens
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## DataCollatorForPermutationLanguageModeling
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[[autodoc]] data.data_collator.DataCollatorForPermutationLanguageModeling
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- numpy_mask_tokens
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- tf_mask_tokens
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- torch_mask_tokens
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## DataCollatorWithFlattening
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[[autodoc]] data.data_collator.DataCollatorWithFlattening
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