Transformers documentation

Multiple choice

Transformers

You are viewing v4.33.0 version. A newer version v4.46.2 is available.

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

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:

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

The task illustrated in this tutorial is supported by the following model architectures:

ALBERT, BERT, BigBird, CamemBERT, CANINE, ConvBERT, Data2VecText, DeBERTa-v2, DistilBERT, ELECTRA, ERNIE, ErnieM, FlauBERT, FNet, Funnel Transformer, I-BERT, Longformer, LUKE, MEGA, Megatron-BERT, MobileBERT, MPNet, MRA, Nezha, Nyströmformer, QDQBert, RemBERT, RoBERTa, RoBERTa-PreLayerNorm, RoCBert, RoFormer, SqueezeBERT, XLM, XLM-RoBERTa, XLM-RoBERTa-XL, XLNet, X-MOD, YOSO

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("bert-base-uncased")

The preprocessing function you want to create needs to:

Make four copies of the sent1 field and combine each of them with sent2 to recreate how a sentence starts.
Combine sent2 with each of the four possible sentence endings.
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)

🤗 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:

Pytorch

Hide Pytorch content

>>> 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

TensorFlow

Hide TensorFlow content

>>> 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

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

Hide Pytorch content

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("bert-base-uncased")

At this point, only three steps remain:

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.
Pass the training arguments to Trainer along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call train() to finetune your model.

>>> training_args = TrainingArguments(
...     output_dir="my_awesome_swag_model",
...     evaluation_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"],
...     tokenizer=tokenizer,
...     data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
...     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

Hide TensorFlow content

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("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

Hide Pytorch content

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("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("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

Hide TensorFlow content

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

>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("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("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'

←Summarization Audio classification→