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# Multiple choice

A multiple choice task is similar to question answering, except several candidate answers are provided along with a context. The model is trained to select the correct answer from multiple inputs given a context.

This guide will show you how to fine-tune [BERT](https://huggingface.co/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.

## Load SWAG dataset

Load the SWAG dataset from the 🤗 Datasets library:

```py
>>> from datasets import load_dataset

>>> swag = load_dataset("swag", "regular")
```

Then take a look at an example:

```py
>>> swag["train"][0]
{'ending0': 'passes by walking down the street playing their instruments.',
 'ending1': 'has heard approaching them.',
 'ending2': "arrives and they're outside dancing and asleep.",
 'ending3': 'turns the lead singer watches the performance.',
 'fold-ind': '3416',
 'gold-source': 'gold',
 'label': 0,
 'sent1': 'Members of the procession walk down the street holding small horn brass instruments.',
 'sent2': 'A drum line',
 'startphrase': 'Members of the procession walk down the street holding small horn brass instruments. A drum line',
 'video-id': 'anetv_jkn6uvmqwh4'}
```

The `sent1` and `sent2` fields show how a sentence begins, and each `ending` field shows how a sentence could end. Given the sentence beginning, the model must pick the correct sentence ending as indicated by the `label` field.

## Preprocess

Load the BERT tokenizer to process the start of each sentence and the four possible endings:

```py
>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
```

The preprocessing function needs to do:

1. Make four copies of the `sent1` field so you can combine each of them with `sent2` to recreate how a sentence starts.
2. Combine `sent2` with each of the four possible sentence endings.
3. Flatten these two lists so you can tokenize them, and then unflatten them afterward so each example has a corresponding `input_ids`, `attention_mask`, and `labels` field.

```py
>>> ending_names = ["ending0", "ending1", "ending2", "ending3"]


>>> def preprocess_function(examples):
...     first_sentences = [[context] * 4 for context in examples["sent1"]]
...     question_headers = examples["sent2"]
...     second_sentences = [
...         [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
...     ]

...     first_sentences = sum(first_sentences, [])
...     second_sentences = sum(second_sentences, [])

...     tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)
...     return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
```

Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. 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)
```

🤗 Transformers doesn't have a data collator for multiple choice, so you will need to create one. You can adapt the [`DataCollatorWithPadding`] to create a batch of examples for multiple choice. It will also *dynamically pad* your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the `tokenizer` function by setting `padding=True`, dynamic padding is more efficient.

`DataCollatorForMultipleChoice` will flatten all the model inputs, apply padding, and then unflatten the results:

<frameworkcontent>
<pt>
```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
```
</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>

## Train

<frameworkcontent>
<pt>
Load BERT with [`AutoModelForMultipleChoice`]:

```py
>>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer

>>> model = AutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
```

<Tip>

If you aren't familiar with fine-tuning a model with Trainer, take a look at the basic tutorial [here](../training#finetune-with-trainer)!

</Tip>

At this point, only three steps remain:

1. Define your training hyperparameters in [`TrainingArguments`].
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, and data collator.
3. Call [`~Trainer.train`] to fine-tune your model.

```py
>>> training_args = TrainingArguments(
...     output_dir="./results",
...     evaluation_strategy="epoch",
...     learning_rate=5e-5,
...     per_device_train_batch_size=16,
...     per_device_eval_batch_size=16,
...     num_train_epochs=3,
...     weight_decay=0.01,
... )

>>> trainer = Trainer(
...     model=model,
...     args=training_args,
...     train_dataset=tokenized_swag["train"],
...     eval_dataset=tokenized_swag["validation"],
...     tokenizer=tokenizer,
...     data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
... )

>>> trainer.train()
```
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~TFPreTrainedModel.prepare_tf_dataset`].

```py
>>> data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
>>> tf_train_set = model.prepare_tf_dataset(
...     tokenized_swag["train"],
...     shuffle=True,
...     batch_size=batch_size,
...     collate_fn=data_collator,
... )

>>> tf_validation_set = model.prepare_tf_dataset(
...     tokenized_swag["validation"],
...     shuffle=False,
...     batch_size=batch_size,
...     collate_fn=data_collator,
... )
```

<Tip>

If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!

</Tip>

Set up an optimizer function, learning rate schedule, and some training hyperparameters:

```py
>>> from transformers import create_optimizer

>>> batch_size = 16
>>> num_train_epochs = 2
>>> total_train_steps = (len(tokenized_swag["train"]) // batch_size) * num_train_epochs
>>> optimizer, schedule = create_optimizer(init_lr=5e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
```

Load BERT with [`TFAutoModelForMultipleChoice`]:

```py
>>> from transformers import TFAutoModelForMultipleChoice

>>> model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-uncased")
```

Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):

```py
>>> model.compile(optimizer=optimizer)
```

Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fine-tune the model:

```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2)
```
</tf>
</frameworkcontent>