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# Translation

<Youtube id="1JvfrvZgi6c"/>

Translation converts a sequence of text from one language to another. It is one of several tasks you can formulate as a sequence-to-sequence problem, a powerful framework that extends to vision and audio tasks. 

This guide will show you how to fine-tune [T5](https://huggingface.co/t5-small) on the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset to translate English text to French.

<Tip>

See the translation [task page](https://huggingface.co/tasks/translation) for more information about its associated models, datasets, and metrics.

</Tip>

## Load OPUS Books dataset

Load the OPUS Books dataset from the 🤗 Datasets library:

```py
>>> from datasets import load_dataset

>>> books = load_dataset("opus_books", "en-fr")
```

Split this dataset into a train and test set:

```py
books = books["train"].train_test_split(test_size=0.2)
```

Then take a look at an example:

```py
>>> books["train"][0]
{'id': '90560',
 'translation': {'en': 'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.',
  'fr': 'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'}}
```

The `translation` field is a dictionary containing the English and French translations of the text.

## Preprocess

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Load the T5 tokenizer to process the language pairs:

```py
>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
```

The preprocessing function needs to:

1. Prefix the input with a prompt so T5 knows this is a translation task. Some models capable of multiple NLP tasks require prompting for specific tasks.
2. Tokenize the input (English) and target (French) separately. You can't tokenize French text with a tokenizer pretrained on an English vocabulary. A context manager will help set the tokenizer to French first before tokenizing it.
3. Truncate sequences to be no longer than the maximum length set by the `max_length` parameter.

```py
>>> source_lang = "en"
>>> target_lang = "fr"
>>> prefix = "translate English to French: "


>>> def preprocess_function(examples):
...     inputs = [prefix + example[source_lang] for example in examples["translation"]]
...     targets = [example[target_lang] for example in examples["translation"]]
...     model_inputs = tokenizer(inputs, max_length=128, truncation=True)

...     with tokenizer.as_target_tokenizer():
...         labels = tokenizer(targets, max_length=128, truncation=True)

...     model_inputs["labels"] = labels["input_ids"]
...     return model_inputs
```

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_books = books.map(preprocess_function, batched=True)
```

<frameworkcontent>
<pt>
Load T5 with [`AutoModelForSeq2SeqLM`]:

```py
>>> from transformers import AutoModelForSeq2SeqLM

>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
```
</pt>
<tf>
Load T5 with [`TFAutoModelForSeq2SeqLM`]:

```py
>>> from transformers import TFAutoModelForSeq2SeqLM

>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")
```
</tf>
</frameworkcontent>

Use [`DataCollatorForSeq2Seq`] to create a batch of examples. 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.

<frameworkcontent>
<pt>

```py
>>> from transformers import DataCollatorForSeq2Seq

>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
```
</pt>
<tf>

```py
>>> from transformers import DataCollatorForSeq2Seq

>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, return_tensors="tf")
```
</tf>
</frameworkcontent>

## Train

<frameworkcontent>
<pt>

<Tip>

If you aren't familiar with fine-tuning a model with the [`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 [`Seq2SeqTrainingArguments`].
2. Pass the training arguments to [`Seq2SeqTrainer`] along with the model, dataset, tokenizer, and data collator.
3. Call [`~Trainer.train`] to fine-tune your model.

```py
>>> from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer

>>> training_args = Seq2SeqTrainingArguments(
...     output_dir="./results",
...     evaluation_strategy="epoch",
...     learning_rate=2e-5,
...     per_device_train_batch_size=16,
...     per_device_eval_batch_size=16,
...     weight_decay=0.01,
...     save_total_limit=3,
...     num_train_epochs=1,
...     fp16=True,
... )

>>> trainer = Seq2SeqTrainer(
...     model=model,
...     args=training_args,
...     train_dataset=tokenized_books["train"],
...     eval_dataset=tokenized_books["test"],
...     tokenizer=tokenizer,
...     data_collator=data_collator,
... )

>>> trainer.train()
```
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~datasets.Dataset.to_tf_dataset`]. Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:

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

>>> tf_test_set = tokenized_books["test"].to_tf_dataset(
...     columns=["attention_mask", "input_ids", "labels"],
...     shuffle=False,
...     batch_size=16,
...     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, AdamWeightDecay

>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```

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_test_set, epochs=3)
```
</tf>
</frameworkcontent>

<Tip>

For a more in-depth example of how to fine-tune a model for translation, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb).

</Tip>
