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# Language modeling

Language modeling predicts words in a sentence. There are two forms of language modeling.

<Youtube id="Vpjb1lu0MDk"/>

Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on the left.

<Youtube id="mqElG5QJWUg"/>

Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally.

This guide will show you how to fine-tune [DistilGPT2](https://huggingface.co/distilgpt2) for causal language modeling and [DistilRoBERTa](https://huggingface.co/distilroberta-base) for masked language modeling on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.

<Tip>

You can fine-tune other architectures for language modeling such as [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), and [BERT](https://huggingface.co/bert-base-uncased), following the same steps presented in this guide!

See the text generation [task page](https://huggingface.co/tasks/text-generation) and fill mask [task page](https://huggingface.co/tasks/fill-mask) for more information about their associated models, datasets, and metrics.

</Tip>

## Load ELI5 dataset

Load only the first 5000 rows of the ELI5 dataset from the 🤗 Datasets library since it is pretty large:

```py
>>> from datasets import load_dataset

>>> eli5 = load_dataset("eli5", split="train_asks[:5000]")
```

Split this dataset into a train and test set:

```py
eli5 = eli5.train_test_split(test_size=0.2)
```

Then take a look at an example:

```py
>>> eli5["train"][0]
{'answers': {'a_id': ['c3d1aib', 'c3d4lya'],
  'score': [6, 3],
  'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
   "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]},
 'answers_urls': {'url': []},
 'document': '',
 'q_id': 'nyxfp',
 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']},
 'subreddit': 'askscience',
 'title': 'Few questions about this space walk photograph.',
 'title_urls': {'url': []}}
```

Notice `text` is a subfield nested inside the `answers` dictionary. When you preprocess the dataset, you will need to extract the `text` subfield into a separate column.

## Preprocess

<Youtube id="ma1TrR7gE7I"/>

For causal language modeling, load the DistilGPT2 tokenizer to process the `text` subfield:

```py
>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
```

<Youtube id="8PmhEIXhBvI"/>

For masked language modeling, load the DistilRoBERTa tokenizer instead:

```py
>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
```

Extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) method:

```py
>>> eli5 = eli5.flatten()
>>> eli5["train"][0]
{'answers.a_id': ['c3d1aib', 'c3d4lya'],
 'answers.score': [6, 3],
 'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
  "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"],
 'answers_urls.url': [],
 'document': '',
 'q_id': 'nyxfp',
 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'],
 'subreddit': 'askscience',
 'title': 'Few questions about this space walk photograph.',
 'title_urls.url': []}
```

Each subfield is now a separate column as indicated by the `answers` prefix. Notice that `answers.text` is a list. Instead of tokenizing each sentence separately, convert the list to a string to jointly tokenize them.

Here is how you can create a preprocessing function to convert the list to a string and truncate sequences to be no longer than DistilGPT2's maximum input length:

```py
>>> def preprocess_function(examples):
...     return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True)
```

Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#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 and increasing the number of processes with `num_proc`. Remove the columns you don't need:

```py
>>> tokenized_eli5 = eli5.map(
...     preprocess_function,
...     batched=True,
...     num_proc=4,
...     remove_columns=eli5["train"].column_names,
... )
```

Now you need a second preprocessing function to capture text truncated from any lengthy examples to prevent loss of information. This preprocessing function should:

- Concatenate all the text.
- Split the concatenated text into smaller chunks defined by `block_size`.

```py
>>> block_size = 128


>>> def group_texts(examples):
...     concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
...     total_length = len(concatenated_examples[list(examples.keys())[0]])
...     result = {
...         k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
...         for k, t in concatenated_examples.items()
...     }
...     result["labels"] = result["input_ids"].copy()
...     return result
```

Apply the `group_texts` function over the entire dataset:

```py
>>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)
```

For causal language modeling, use [`DataCollatorForLanguageModeling`] to create a batch of examples. It will also *dynamically pad* your text 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>
You can use the end of sequence token as the padding token, and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:

```py
>>> from transformers import DataCollatorForLanguageModeling

>>> tokenizer.pad_token = tokenizer.eos_token
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
```

For masked language modeling, use the same [`DataCollatorForLanguageModeling`] except you should specify `mlm_probability` to randomly mask tokens each time you iterate over the data.

```py
>>> from transformers import DataCollatorForLanguageModeling

>>> tokenizer.pad_token = tokenizer.eos_token
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)
```
</pt>
<tf>
You can use the end of sequence token as the padding token, and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:

```py
>>> from transformers import DataCollatorForLanguageModeling

>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
```

For masked language modeling, use the same [`DataCollatorForLanguageModeling`] except you should specify `mlm_probability` to randomly mask tokens each time you iterate over the data.

```py
>>> from transformers import DataCollatorForLanguageModeling

>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
```
</tf>
</frameworkcontent>

## Causal language modeling

Causal language modeling is frequently used for text generation. This section shows you how to fine-tune [DistilGPT2](https://huggingface.co/distilgpt2) to generate new text.

### Fine-tune with Trainer

Load DistilGPT2 with [`AutoModelForCausalLM`]:

```py
>>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer

>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
```

<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 [`TrainingArguments`].
2. Pass the training arguments to [`Trainer`] along with the model, datasets, and data collator.
3. Call [`~Trainer.train`] to fine-tune your model.

```py
>>> training_args = TrainingArguments(
...     output_dir="./results",
...     evaluation_strategy="epoch",
...     learning_rate=2e-5,
...     weight_decay=0.01,
... )

>>> trainer = Trainer(
...     model=model,
...     args=training_args,
...     train_dataset=lm_dataset["train"],
...     eval_dataset=lm_dataset["test"],
...     data_collator=data_collator,
... )

>>> trainer.train()
```

### Fine-tune with TensorFlow

To fine-tune a model in TensorFlow is just as easy, with only a few differences.

<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>

Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#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 = lm_dataset["train"].to_tf_dataset(
...     columns=["attention_mask", "input_ids", "labels"],
...     dummy_labels=True,
...     shuffle=True,
...     batch_size=16,
...     collate_fn=data_collator,
... )

>>> tf_test_set = lm_dataset["test"].to_tf_dataset(
...     columns=["attention_mask", "input_ids", "labels"],
...     dummy_labels=True,
...     shuffle=False,
...     batch_size=16,
...     collate_fn=data_collator,
... )
```

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

```py
>>> from transformers import create_optimizer, AdamWeightDecay

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

Load DistilGPT2 with [`TFAutoModelForCausalLM`]:

```py
>>> from transformers import TFAutoModelForCausalLM

>>> model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")
```

Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):

```py
>>> import tensorflow as tf

>>> 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)
```

## Masked language modeling

Masked language modeling is also known as a fill-mask task because it predicts a masked token in a sequence. Models for masked language modeling require a good contextual understanding of an entire sequence instead of only the left context. This section shows you how to fine-tune [DistilRoBERTa](https://huggingface.co/distilroberta-base) to predict a masked word.

### Fine-tune with Trainer

Load DistilRoBERTa with [`AutoModelForMaskedlM`]:

```py
>>> from transformers import AutoModelForMaskedLM

>>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
```

<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 [`TrainingArguments`].
2. Pass the training arguments to [`Trainer`] along with the model, datasets, and data collator.
3. Call [`~Trainer.train`] to fine-tune your model.

```py
>>> training_args = TrainingArguments(
...     output_dir="./results",
...     evaluation_strategy="epoch",
...     learning_rate=2e-5,
...     num_train_epochs=3,
...     weight_decay=0.01,
... )

>>> trainer = Trainer(
...     model=model,
...     args=training_args,
...     train_dataset=lm_dataset["train"],
...     eval_dataset=lm_dataset["test"],
...     data_collator=data_collator,
... )

>>> trainer.train()
```

### Fine-tune with TensorFlow

To fine-tune a model in TensorFlow is just as easy, with only a few differences.

<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>

Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#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 = lm_dataset["train"].to_tf_dataset(
...     columns=["attention_mask", "input_ids", "labels"],
...     dummy_labels=True,
...     shuffle=True,
...     batch_size=16,
...     collate_fn=data_collator,
... )

>>> tf_test_set = lm_dataset["test"].to_tf_dataset(
...     columns=["attention_mask", "input_ids", "labels"],
...     dummy_labels=True,
...     shuffle=False,
...     batch_size=16,
...     collate_fn=data_collator,
... )
```

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

```py
>>> from transformers import create_optimizer, AdamWeightDecay

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

Load DistilRoBERTa with [`TFAutoModelForMaskedLM`]:

```py
>>> from transformers import TFAutoModelForMaskedLM

>>> model = TFAutoModelForCausalLM.from_pretrained("distilroberta-base")
```

Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):

```py
>>> import tensorflow as tf

>>> 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)
```

<Tip>

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

</Tip>