#
Multiple choice

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

This guide will show you how to:

1. Finetune BERT on the `regular` configuration of the SWAG dataset to select the best answer given multiple options and some context.
2. Use your finetuned model for inference.

Before you begin, make sure you have all the necessary libraries installed:

    pip install transformers datasets evaluate

We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:

    >>> from huggingface_hub import notebook_login

    >>> notebook_login()

##
Load SWAG dataset

Start by loading the `regular` configuration of the SWAG dataset from the 🤗 Datasets library:

    >>> from datasets import load_dataset

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

Then take a look at an example:

    >>> 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'}

While it looks like there are a lot of fields here, it is actually pretty straightforward:

- `sent1` and `sent2`: these fields show how a sentence starts, and if you put the two together, you get the `startphrase` field.
- `ending`: suggests a possible ending for how a sentence can end, but only one of them is correct.
- `label`: identifies the correct sentence ending.

##
Preprocess

The next step is to load a BERT tokenizer to process the sentence starts and the four possible endings:

    >>> from transformers import AutoTokenizer

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

The preprocessing function you want to create needs to:

1. Make four copies of the `sent1` field and 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.

    >>> 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()}

To apply the preprocessing function over the entire dataset, use 🤗 Datasets map method. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:

    >>> tokenized_swag = swag.map(preprocess_function, batched=True)

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.

    >>> from transformers import DataCollatorForMultipleChoice
    >>> collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)

##
Evaluate

Including a metric during training is often helpful for evaluating your model’s performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):

    >>> import evaluate

    >>> accuracy = evaluate.load("accuracy")

Then create a function that passes your predictions and labels to compute to calculate the accuracy:

    >>> import numpy as np

    >>> def compute_metrics(eval_pred):
    ...     predictions, labels = eval_pred
    ...     predictions = np.argmax(predictions, axis=1)
    ...     return accuracy.compute(predictions=predictions, references=labels)

Your `compute_metrics` function is ready to go now, and you’ll return to it when you setup your training.

##
Train

Pytorch
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If you aren’t familiar with finetuning a model with the Trainer, take a look at the basic tutorial here!

You’re ready to start training your model now! Load BERT with AutoModelForMultipleChoice:

    >>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer

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

At this point, only three steps remain:

1. Define your training hyperparameters in TrainingArguments. The only required parameter is `output_dir` which specifies where to save your model. You’ll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the Trainer will evaluate the accuracy and save the training checkpoint.
2. Pass the training arguments to Trainer along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call train() to finetune your model.

    >>> training_args = TrainingArguments(
    ...     output_dir="my_awesome_swag_model",
    ...     eval_strategy="epoch",
    ...     save_strategy="epoch",
    ...     load_best_model_at_end=True,
    ...     learning_rate=5e-5,
    ...     per_device_train_batch_size=16,
    ...     per_device_eval_batch_size=16,
    ...     num_train_epochs=3,
    ...     weight_decay=0.01,
    ...     push_to_hub=True,
    ... )

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

    >>> trainer.train()

Once training is completed, share your model to the Hub with the push_to_hub() method so everyone can use your model:

    >>> trainer.push_to_hub()

TensorFlow
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If you aren’t familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

    >>> 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)

Then you can load BERT with TFAutoModelForMultipleChoice:

    >>> from transformers import TFAutoModelForMultipleChoice

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

Convert your datasets to the `tf.data.Dataset` format with prepare_tf_dataset():

    >>> 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,
    ... )

Configure the model for training with `compile`. Note that Transformers models all have a default task-relevant loss function, so you don’t need to specify one unless you want to:

    >>> model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.

Pass your `compute_metrics` function to KerasMetricCallback:

    >>> from transformers.keras_callbacks import KerasMetricCallback

    >>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the PushToHubCallback:

    >>> from transformers.keras_callbacks import PushToHubCallback

    >>> push_to_hub_callback = PushToHubCallback(
    ...     output_dir="my_awesome_model",
    ...     tokenizer=tokenizer,
    ... )

Then bundle your callbacks together:

    >>> callbacks = [metric_callback, push_to_hub_callback]

Finally, you’re ready to start training your model! Call `fit` with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

    >>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for multiple choice, take a look at the corresponding PyTorch notebook or TensorFlow notebook.

##
Inference

Great, now that you’ve finetuned a model, you can use it for inference!

Come up with some text and two candidate answers:

    >>> prompt = "France has a bread law, Le Décret Pain, with strict rules on what is allowed in a traditional baguette."
    >>> candidate1 = "The law does not apply to croissants and brioche."
    >>> candidate2 = "The law applies to baguettes."

Pytorch
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Tokenize each prompt and candidate answer pair and return PyTorch tensors. You should also create some `labels`:

    >>> from transformers import AutoTokenizer

    >>> tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_swag_model")
    >>> inputs = tokenizer([[prompt, candidate1], [prompt, candidate2]], return_tensors="pt", padding=True)
    >>> labels = torch.tensor(0).unsqueeze(0)

Pass your inputs and labels to the model and return the `logits`:

    >>> from transformers import AutoModelForMultipleChoice

    >>> model = AutoModelForMultipleChoice.from_pretrained("username/my_awesome_swag_model")
    >>> outputs = model(**{k: v.unsqueeze(0) for k, v in inputs.items()}, labels=labels)
    >>> logits = outputs.logits

Get the class with the highest probability:

    >>> predicted_class = logits.argmax().item()
    >>> predicted_class
    0

TensorFlow
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Tokenize each prompt and candidate answer pair and return TensorFlow tensors:

    >>> from transformers import AutoTokenizer

    >>> tokenizer = AutoTokenizer.from_pretrained("username/my_awesome_swag_model")
    >>> inputs = tokenizer([[prompt, candidate1], [prompt, candidate2]], return_tensors="tf", padding=True)

Pass your inputs to the model and return the `logits`:

    >>> from transformers import TFAutoModelForMultipleChoice

    >>> model = TFAutoModelForMultipleChoice.from_pretrained("username/my_awesome_swag_model")
    >>> inputs = {k: tf.expand_dims(v, 0) for k, v in inputs.items()}
    >>> outputs = model(inputs)
    >>> logits = outputs.logits

Get the class with the highest probability:

    >>> predicted_class = int(tf.math.argmax(logits, axis=-1)[0])
    >>> predicted_class
    0

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