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Move DataCollatorForMultipleChoice from the docs to the package (#34763)

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* Add implementation for DataCollatorForMultipleChoice based on docs.

* Add DataCollatorForMultipleChoice to import structure.

* Remove custom DataCollatorForMultipleChoice implementations from example scripts.

* Remove custom implementations of DataCollatorForMultipleChoice from docs in English, Spanish, Japanese and Korean.

* Refactor torch version of DataCollatorForMultipleChoice to be more easily understandable.

* Apply suggested changes and run make fixup.

* fix copies, style and fixup

* add missing documentation

* nits

* fix docstring

* style

* nits

* isort

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>
This commit is contained in:
Thomas Bauwens
2025-02-13 12:01:28 +01:00
committed by GitHub
parent 35c155052d
commit 8f137b2427
25 changed files with 361 additions and 670 deletions
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95
docs/source/en/tasks/multiple_choice.md
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@@ -109,99 +109,14 @@ The preprocessing function you want to create needs to:
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] method. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
```py
tokenized_swag = swag.map(preprocess_function, batched=True)
>>> tokenized_swag = swag.map(preprocess_function, batched=True)
```
🤗 Transformers doesn't have a data collator for multiple choice, so you'll need to adapt the [`DataCollatorWithPadding`] to create a batch of examples. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length.
`DataCollatorForMultipleChoice` flattens all the model inputs, applies padding, and then unflattens the results:
<frameworkcontent>
<pt>
To create a batch of examples, it's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length. [`DataCollatorForMultipleChoice`] flattens all the model inputs, applies padding, and then unflattens the results.
```py
>>> from dataclasses import dataclass
>>> from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
>>> from typing import Optional, Union
>>> import torch
>>> @dataclass
... class DataCollatorForMultipleChoice:
... """
... Data collator that will dynamically pad the inputs for multiple choice received.
... """
... tokenizer: PreTrainedTokenizerBase
... padding: Union[bool, str, PaddingStrategy] = True
... max_length: Optional[int] = None
... pad_to_multiple_of: Optional[int] = None
... def __call__(self, features):
... label_name = "label" if "label" in features[0].keys() else "labels"
... labels = [feature.pop(label_name) for feature in features]
... batch_size = len(features)
... num_choices = len(features[0]["input_ids"])
... flattened_features = [
... [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
... ]
... flattened_features = sum(flattened_features, [])
... batch = self.tokenizer.pad(
... flattened_features,
... padding=self.padding,
... max_length=self.max_length,
... pad_to_multiple_of=self.pad_to_multiple_of,
... return_tensors="pt",
... )
... batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
... batch["labels"] = torch.tensor(labels, dtype=torch.int64)
... return batch
>>> from transformers import DataCollatorForMultipleChoice
>>> collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
```
</pt>
<tf>
```py
>>> from dataclasses import dataclass
>>> from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
>>> from typing import Optional, Union
>>> import tensorflow as tf
>>> @dataclass
... class DataCollatorForMultipleChoice:
... """
... Data collator that will dynamically pad the inputs for multiple choice received.
... """
... tokenizer: PreTrainedTokenizerBase
... padding: Union[bool, str, PaddingStrategy] = True
... max_length: Optional[int] = None
... pad_to_multiple_of: Optional[int] = None
... def __call__(self, features):
... label_name = "label" if "label" in features[0].keys() else "labels"
... labels = [feature.pop(label_name) for feature in features]
... batch_size = len(features)
... num_choices = len(features[0]["input_ids"])
... flattened_features = [
... [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
... ]
... flattened_features = sum(flattened_features, [])
... batch = self.tokenizer.pad(
... flattened_features,
... padding=self.padding,
... max_length=self.max_length,
... pad_to_multiple_of=self.pad_to_multiple_of,
... return_tensors="tf",
... )
... batch = {k: tf.reshape(v, (batch_size, num_choices, -1)) for k, v in batch.items()}
... batch["labels"] = tf.convert_to_tensor(labels, dtype=tf.int64)
... return batch
```
</tf>
</frameworkcontent>
## Evaluate
@@ -271,7 +186,7 @@ At this point, only three steps remain:
... train_dataset=tokenized_swag["train"],
... eval_dataset=tokenized_swag["validation"],
... processing_class=tokenizer,
... data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
... data_collator=collator,
... compute_metrics=compute_metrics,
... )