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

[[open-in-colab]]

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.

```bash
pip install datasets
```

Learn how to fine-tune a model for:

- [seq_imdb](#seq_imdb)
- [tok_ner](#tok_ner)
- [qa_squad](#qa_squad)

<a id='seq_imdb'></a>

## 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.

<Tip>

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).

</Tip>

### Load IMDb dataset

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

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

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

```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
[`AutoTokenizer`] because we will eventually train a classifier using a pretrained [DistilBERT](https://huggingface.co/distilbert-base-uncased) model:

```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:

```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:

```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:

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

### Fine-tune with the Trainer API

Now load your model with the [`AutoModelForSequenceClassification`] class along with the number of expected labels:

```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 [`TrainingArguments`].
2. Pass the training arguments to a [`Trainer`] along with the model, dataset, tokenizer, and data collator.
3. Call [`Trainer.train()`] to fine-tune your model.

```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!

```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:

```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:

```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 [`TFAutoModelForSequenceClassification`] class along with the number of expected labels:

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

Compile the model:

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

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

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

<a id='tok_ner'></a>

## 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.

<Tip>

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).

</Tip>

### Load WNUT 17 dataset

Load the WNUT 17 dataset from the 🤗 Datasets library:

```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:

```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:

```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 [`AutoTokenizer`]:

```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:

```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:

```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:

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

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

```python
from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer)
```

### Fine-tune with the Trainer API

Load your model with the [`AutoModelForTokenClassification`] class along with the number of expected labels:

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

Gather your training arguments in [`TrainingArguments`]:

```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 [`Trainer`]:

```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:

```python
trainer.train()
```

### Fine-tune with TensorFlow

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

```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`:

```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 [`TFAutoModelForTokenClassification`] class along with the number of expected labels:

```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:

```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:

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

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

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

<a id='qa_squad'></a>

## 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.

<Tip>

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).

</Tip>

### Load SQuAD dataset

Load the SQuAD dataset from the 🤗 Datasets library:

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

Take a look at an example from the dataset:

```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 [`AutoTokenizer`]:

```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:

```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:

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

Batch the processed examples together:

```python
from transformers import default_data_collator
data_collator = default_data_collator
```

### Fine-tune with the Trainer API

Load your model with the [`AutoModelForQuestionAnswering`] class:

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

Gather your training arguments in [`TrainingArguments`]:

```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 [`Trainer`]:

```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:

```python
trainer.train()
```

### Fine-tune with TensorFlow

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

```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:

```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:

```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 [`TFAutoModelForQuestionAnswering`] class:

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

Compile the model:

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

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

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