custom_datasets.rst

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How to fine-tune a model for common downstream tasks
=======================================================================================================================

This guide will show you how to fine-tune 🤗 Transformers models for common downstream tasks. You will use the 🤗
Datasets library to quickly load and preprocess the datasets, getting them ready for training with PyTorch and
TensorFlow.

Before you begin, make sure you have the 🤗 Datasets library installed. For more detailed installation instructions,
refer to the 🤗 Datasets `installation page <https://huggingface.co/docs/datasets/installation.html>`_. All of the
examples in this guide will use 🤗 Datasets to load and preprocess a dataset.

.. code-block:: bash

    pip install datasets

Learn how to fine-tune a model for:

- :ref:`seq_imdb`
- :ref:`tok_ner`
- :ref:`qa_squad`

.. _seq_imdb:

Sequence classification with IMDb reviews
-----------------------------------------------------------------------------------------------------------------------

Sequence classification refers to the task of classifying sequences of text according to a given number of classes. In
this example, learn how to fine-tune a model on the `IMDb dataset <https://huggingface.co/datasets/imdb>`_ to determine
whether a review is positive or negative.

.. note::

    For a more in-depth example of how to fine-tune a model for text classification, take a look at the corresponding
    `PyTorch notebook
    <https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb>`__
    or `TensorFlow notebook
    <https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb>`__.

Load IMDb dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The 🤗 Datasets library makes it simple to load a dataset:

.. code-block:: python

    from datasets import load_dataset
    imdb = load_dataset("imdb")

This loads a ``DatasetDict`` object which you can index into to view an example:

.. code-block:: python

    imdb["train"][0]
    {'label': 1,
     'text': 'Bromwell High is a cartoon comedy. It ran at the same time as some other programs about school life, such as "Teachers". My 35 years in the teaching profession lead me to believe that Bromwell High\'s satire is much closer to reality than is "Teachers". The scramble to survive financially, the insightful students who can see right through their pathetic teachers\' pomp, the pettiness of the whole situation, all remind me of the schools I knew and their students. When I saw the episode in which a student repeatedly tried to burn down the school, I immediately recalled ......... at .......... High. A classic line: INSPECTOR: I\'m here to sack one of your teachers. STUDENT: Welcome to Bromwell High. I expect that many adults of my age think that Bromwell High is far fetched. What a pity that it isn\'t!'
    }

Preprocess
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The next step is to tokenize the text into a readable format by the model. It is important to load the same tokenizer a
model was trained with to ensure appropriately tokenized words. Load the DistilBERT tokenizer with the
:class:`~transformers.AutoTokenizer` because we will eventually train a classifier using a pretrained `DistilBERT
<https://huggingface.co/distilbert-base-uncased>`_ model:

.. code-block:: python

    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

Now that you have instantiated a tokenizer, create a function that will tokenize the text. You should also truncate
longer sequences in the text to be no longer than the model's maximum input length:

.. code-block:: python

    def preprocess_function(examples):
        return tokenizer(examples["text"], truncation=True)

Use 🤗 Datasets ``map`` function to apply the preprocessing function to the entire dataset. You can also set
``batched=True`` to apply the preprocessing function to multiple elements of the dataset at once for faster
preprocessing:

.. code-block:: python

    tokenized_imdb = imdb.map(preprocess_function, batched=True)

Lastly, pad your text so they are a uniform length. While it is possible to pad your text in the ``tokenizer`` function
by setting ``padding=True``, it is more efficient to only pad the text to the length of the longest element in its
batch. This is known as **dynamic padding**. You can do this with the ``DataCollatorWithPadding`` function:

.. code-block:: python

    from transformers import DataCollatorWithPadding
    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

Fine-tune with the Trainer API
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Now load your model with the :class:`~transformers.AutoModel` class along with the number of expected labels:

.. code-block:: python

    from transformers import AutoModelForSequenceClassification
    model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)

At this point, only three steps remain:

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

.. code-block:: python

    from transformers import TrainingArguments, Trainer

    training_args = TrainingArguments(
        output_dir='./results',
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_train_epochs=5,
        weight_decay=0.01,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_imdb["train"],
        eval_dataset=tokenized_imdb["test"],
        tokenizer=tokenizer,
        data_collator=data_collator,
    )

    trainer.train()

Fine-tune with TensorFlow
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Fine-tuning with TensorFlow is just as easy, with only a few differences.

Start by batching the processed examples together with dynamic padding using the ``DataCollatorWithPadding`` function.
Make sure you set ``return_tensors="tf"`` to return ``tf.Tensor`` outputs instead of PyTorch tensors!

.. code-block:: python

    from transformers import DataCollatorWithPadding
    data_collator = DataCollatorWithPadding(tokenizer, return_tensors="tf")

Next, convert your datasets to the ``tf.data.Dataset`` format with ``to_tf_dataset``. Specify inputs and labels in the
``columns`` argument:

.. code-block:: python

    tf_train_dataset = tokenized_imdb["train"].to_tf_dataset(
        columns=['attention_mask', 'input_ids', 'label'],
        shuffle=True,
        batch_size=16,
        collate_fn=data_collator,
    )

    tf_validation_dataset = tokenized_imdb["train"].to_tf_dataset(
        columns=['attention_mask', 'input_ids', 'label'],
        shuffle=False,
        batch_size=16,
        collate_fn=data_collator,
    )

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

.. code-block:: python

    from transformers import create_optimizer
    import tensorflow as tf

    batch_size = 16
    num_epochs = 5
    batches_per_epoch = len(tokenized_imdb["train"]) // batch_size
    total_train_steps = int(batches_per_epoch * num_epochs)
    optimizer, schedule = create_optimizer(
        init_lr=2e-5, 
        num_warmup_steps=0, 
        num_train_steps=total_train_steps
    )

Load your model with the :class:`~transformers.TFAutoModel` class along with the number of expected labels:

.. code-block:: python

    from transformers import TFAutoModelForSequenceClassification
    model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)

Compile the model:

.. code-block:: python

    import tensorflow as tf
    model.compile(optimizer=optimizer)

Finally, fine-tune the model by calling ``model.fit``:

.. code-block:: python

    model.fit(
        tf_train_set,
        validation_data=tf_validation_set,
        epochs=num_train_epochs,
    )

.. _tok_ner:

Token classification with WNUT emerging entities
-----------------------------------------------------------------------------------------------------------------------

Token classification refers to the task of classifying individual tokens in a sentence. One of the most common token
classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence,
such as a person, location, or organization. In this example, learn how to fine-tune a model on the `WNUT 17
<https://huggingface.co/datasets/wnut_17>`_ dataset to detect new entities.

.. note::

    For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
    `PyTorch notebook
    <https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb>`__
    or `TensorFlow notebook
    <https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb>`__.

Load WNUT 17 dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Load the WNUT 17 dataset from the 🤗 Datasets library:

.. code-block:: python

    from datasets import load_dataset
    wnut = load_dataset("wnut_17")

A quick look at the dataset shows the labels associated with each word in the sentence:

.. code-block:: python

    wnut["train"][0]
    {'id': '0',
     'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
     'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
    }

View the specific NER tags by:

.. code-block:: python

    label_list = wnut["train"].features[f"ner_tags"].feature.names
    label_list
    ['O',
     'B-corporation',
     'I-corporation',
     'B-creative-work',
     'I-creative-work',
     'B-group',
     'I-group',
     'B-location',
     'I-location',
     'B-person',
     'I-person',
     'B-product',
     'I-product'
    ]

A letter prefixes each NER tag which can mean:

* ``B-`` indicates the beginning of an entity.
* ``I-`` indicates a token is contained inside the same entity (e.g., the ``State`` token is a part of an entity like
  ``Empire State Building``).
* ``0`` indicates the token doesn't correspond to any entity.

Preprocess
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Now you need to tokenize the text. Load the DistilBERT tokenizer with an :class:`~transformers.AutoTokenizer`:

.. code-block:: python

    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

Since the input has already been split into words, set ``is_split_into_words=True`` to tokenize the words into
subwords:

.. code-block:: python

    tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
    tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
    tokens
    ['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']

The addition of the special tokens ``[CLS]`` and ``[SEP]`` and subword tokenization creates a mismatch between the
input and labels. Realign the labels and tokens by:

1. Mapping all tokens to their corresponding word with the ``word_ids`` method.
2. Assigning the label ``-100`` to the special tokens ``[CLS]`` and ``[SEP]``` so the PyTorch loss function ignores
   them.
3. Only labeling the first token of a given word. Assign ``-100`` to the other subtokens from the same word.

Here is how you can create a function that will realign the labels and tokens:

.. code-block:: python

    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)

        labels = []
        for i, label in enumerate(examples[f"ner_tags"]):
            word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
            previous_word_idx = None
            label_ids = []
            for word_idx in word_ids:                            # Set the special tokens to -100.
                if word_idx is None:
                    label_ids.append(-100)
                elif word_idx != previous_word_idx:              # Only label the first token of a given word.
                    label_ids.append(label[word_idx])

            labels.append(label_ids)

        tokenized_inputs["labels"] = labels
        return tokenized_inputs

Now tokenize and align the labels over the entire dataset with 🤗 Datasets ``map`` function:

.. code-block:: python

    tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)

Finally, pad your text and labels, so they are a uniform length:

.. code-block:: python

    from transformers import DataCollatorForTokenClassification
    data_collator = DataCollatorForTokenClassification(tokenizer)

Fine-tune with the Trainer API
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Load your model with the :class:`~transformers.AutoModel` class along with the number of expected labels:

.. code-block:: python

    from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
    model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))

Gather your training arguments in :class:`~transformers.TrainingArguments`:

.. code-block:: python

    training_args = TrainingArguments(
        output_dir='./results',
        evaluation_strategy="epoch",
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_train_epochs=3,
        weight_decay=0.01,
    )

Collect your model, training arguments, dataset, data collator, and tokenizer in :class:`~transformers.Trainer`:

.. code-block:: python

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_wnut["train"],
        eval_dataset=tokenized_wnut["test"],
        data_collator=data_collator,
        tokenizer=tokenizer,
    )

Fine-tune your model:

.. code-block:: python

    trainer.train()

Fine-tune with TensorFlow
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Batch your examples together and pad your text and labels, so they are a uniform length:

.. code-block:: python

    from transformers import DataCollatorForTokenClassification
    data_collator = DataCollatorForTokenClassification(tokenizer, return_tensors="tf")

Convert your datasets to the ``tf.data.Dataset`` format with ``to_tf_dataset``:

.. code-block:: python

    tf_train_set = tokenized_wnut["train"].to_tf_dataset(
        columns=["attention_mask", "input_ids", "labels"],
        shuffle=True,
        batch_size=16,
        collate_fn=data_collator,
    )

    tf_validation_set = tokenized_wnut["validation"].to_tf_dataset(
        columns=["attention_mask", "input_ids", "labels"],
        shuffle=False,
        batch_size=16,
        collate_fn=data_collator,
    )

Load the model with the :class:`~transformers.TFAutoModel` class along with the number of expected labels:

.. code-block:: python

    from transformers import TFAutoModelForTokenClassification
    model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))

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

.. code-block:: python

    from transformers import create_optimizer

    batch_size = 16
    num_train_epochs = 3
    num_train_steps = (len(tokenized_datasets["train"]) // batch_size) * num_train_epochs
    optimizer, lr_schedule = create_optimizer(
        init_lr=2e-5,
        num_train_steps=num_train_steps,
        weight_decay_rate=0.01,
        num_warmup_steps=0,
    )

Compile the model:

.. code-block:: python

    import tensorflow as tf
    model.compile(optimizer=optimizer)

Call ``model.fit`` to fine-tune your model:

.. code-block:: python

    model.fit(
        tf_train_set,
        validation_data=tf_validation_set,
        epochs=num_train_epochs,
    )

.. _qa_squad:

Question Answering with SQuAD
-----------------------------------------------------------------------------------------------------------------------

There are many types of question answering (QA) tasks. Extractive QA focuses on identifying the answer from the text
given a question. In this example, learn how to fine-tune a model on the `SQuAD
<https://huggingface.co/datasets/squad>`_ dataset.

.. note::

    For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding
    `PyTorch notebook
    <https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb>`__
    or `TensorFlow notebook
    <https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb>`__.

Load SQuAD dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Load the SQuAD dataset from the 🤗 Datasets library:

.. code-block:: python

    from datasets import load_dataset
    squad = load_dataset("squad")

Take a look at an example from the dataset:

.. code-block:: python

    squad["train"][0]
    {'answers': {'answer_start': [515], 'text': ['Saint Bernadette Soubirous']},
     'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
     'id': '5733be284776f41900661182',
     'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
     'title': 'University_of_Notre_Dame'
    }

Preprocess
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Load the DistilBERT tokenizer with an :class:`~transformers.AutoTokenizer`:

.. code-block:: python

    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

There are a few things to be aware of when preprocessing text for question answering:

1. Some examples in a dataset may have a very long ``context`` that exceeds the maximum input length of the model. You
   can deal with this by truncating the ``context`` and set ``truncation="only_second"``.
2. Next, you need to map the start and end positions of the answer to the original context. Set
   ``return_offset_mapping=True`` to handle this.
3. With the mapping in hand, you can find the start and end tokens of the answer. Use the ``sequence_ids`` method to
   find which part of the offset corresponds to the question, and which part of the offset corresponds to the context.

Assemble everything in a preprocessing function as shown below:

.. code-block:: python

    def preprocess_function(examples):
        questions = [q.strip() for q in examples["question"]]
        inputs = tokenizer(
            questions,
            examples["context"],
            max_length=384,
            truncation="only_second",
            return_offsets_mapping=True,
            padding="max_length",
        )

        offset_mapping = inputs.pop("offset_mapping")
        answers = examples["answers"]
        start_positions = []
        end_positions = []

        for i, offset in enumerate(offset_mapping):
            answer = answers[i]
            start_char = answer["answer_start"][0]
            end_char = answer["answer_start"][0] + len(answer["text"][0])
            sequence_ids = inputs.sequence_ids(i)

            # Find the start and end of the context
            idx = 0
            while sequence_ids[idx] != 1:
                idx += 1
            context_start = idx
            while sequence_ids[idx] == 1:
                idx += 1
            context_end = idx - 1

            # If the answer is not fully inside the context, label it (0, 0)
            if offset[context_start][0] > end_char or offset[context_end][1] < start_char:
                start_positions.append(0)
                end_positions.append(0)
            else:
                # Otherwise it's the start and end token positions
                idx = context_start
                while idx <= context_end and offset[idx][0] <= start_char:
                    idx += 1
                start_positions.append(idx - 1)

                idx = context_end
                while idx >= context_start and offset[idx][1] >= end_char:
                    idx -= 1
                end_positions.append(idx + 1)

        inputs["start_positions"] = start_positions
        inputs["end_positions"] = end_positions
        return inputs

Apply the preprocessing function over the entire dataset with 🤗 Datasets ``map`` function:

.. code-block:: python

    tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad["train"].column_names)

Batch the processed examples together:

.. code-block:: python

    from transformers import default_data_collator
    data_collator = default_data_collator

Fine-tune with the Trainer API
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Load your model with the :class:`~transformers.AutoModel` class:

.. code-block:: python

    from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
    model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")

Gather your training arguments in :class:`~transformers.TrainingArguments`:

.. code-block:: python

    training_args = TrainingArguments(
        output_dir='./results',
        evaluation_strategy="epoch",
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_train_epochs=3,
        weight_decay=0.01,
    )

Collect your model, training arguments, dataset, data collator, and tokenizer in :class:`~transformers.Trainer`:

.. code-block:: python

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_squad["train"],
        eval_dataset=tokenized_squad["validation"],
        data_collator=data_collator,
        tokenizer=tokenizer,
    )

Fine-tune your model:

.. code-block:: python

    trainer.train()

Fine-tune with TensorFlow
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Batch the processed examples together with a TensorFlow default data collator:

.. code-block:: python

    from transformers.data.data_collator import tf_default_collator
    data_collator = tf_default_collator

Convert your datasets to the ``tf.data.Dataset`` format with the ``to_tf_dataset`` function:

.. code-block:: python

    tf_train_set = tokenized_squad["train"].to_tf_dataset(
        columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
        dummy_labels=True,
        shuffle=True,
        batch_size=16,
        collate_fn=data_collator,
    )

    tf_validation_set = tokenized_squad["validation"].to_tf_dataset(
        columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
        dummy_labels=True,
        shuffle=False,
        batch_size=16,
        collate_fn=data_collator,
    )

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

.. code-block:: python

    from transformers import create_optimizer

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

Load your model with the :class:`~transformers.TFAutoModel` class:

.. code-block:: python

    from transformers import TFAutoModelForQuestionAnswering
    model = TFAutoModelForQuestionAnswering("distilbert-base-uncased")

Compile the model:

.. code-block:: python

    import tensorflow as tf
    model.compile(optimizer=optimizer)

Call ``model.fit`` to fine-tune the model:

.. code-block:: python

    model.fit(
        tf_train_set,
        validation_data=tf_validation_set,
        epochs=num_train_epochs,
    )