Transformers documentation

Masked language modeling

Transformers

You are viewing v4.33.2 version. A newer version v4.47.1 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

Masked language modeling

Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This means the model has full access to the tokens on the left and right. Masked language modeling is great for tasks that require a good contextual understanding of an entire sequence. BERT is an example of a masked language model.

This guide will show you how to:

Finetune DistilRoBERTa on the r/askscience subset of the ELI5 dataset.
Use your finetuned model for inference.

You can finetune other architectures for masked language modeling following the same steps in this guide. Choose one of the following architectures:

ALBERT, BART, BERT, BigBird, CamemBERT, ConvBERT, Data2VecText, DeBERTa, DeBERTa-v2, DistilBERT, ELECTRA, ERNIE, ESM, FlauBERT, FNet, Funnel Transformer, I-BERT, LayoutLM, Longformer, LUKE, mBART, MEGA, Megatron-BERT, MobileBERT, MPNet, MRA, MVP, Nezha, Nyströmformer, Perceiver, QDQBert, Reformer, RemBERT, RoBERTa, RoBERTa-PreLayerNorm, RoCBert, RoFormer, SqueezeBERT, TAPAS, Wav2Vec2, XLM, XLM-RoBERTa, XLM-RoBERTa-XL, X-MOD, YOSO

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate

We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:

>>> from huggingface_hub import notebook_login

>>> notebook_login()

Load ELI5 dataset

Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library. This’ll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.

>>> from datasets import load_dataset

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

Split the dataset’s train_asks split into a train and test set with the train_test_split method:

>>> eli5 = eli5.train_test_split(test_size=0.2)

Then take a look at an example:

>>> 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': []}}

While this may look like a lot, you’re only really interested in the text field. What’s cool about language modeling tasks is you don’t need labels (also known as an unsupervised task) because the next word is the label.

Preprocess

For masked language modeling, the next step is to load a DistilRoBERTa tokenizer to process the text subfield:

>>> from transformers import AutoTokenizer

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

You’ll notice from the example above, the text field is actually nested inside answers. This means you’ll need to e xtract the text subfield from its nested structure with the flatten method:

>>> 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, and the text field is a list now. Instead of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them.

Here is a first preprocessing function to join the list of strings for each example and tokenize the result:

>>> def preprocess_function(examples):
...     return tokenizer([" ".join(x) for x in examples["answers.text"]])

To apply this preprocessing function over the entire dataset, use the 🤗 Datasets map method. 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 any columns you don’t need:

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

This dataset contains the token sequences, but some of these are longer than the maximum input length for the model.

You can now use a second preprocessing function to

concatenate all the sequences
split the concatenated sequences into shorter chunks defined by block_size, which should be both shorter than the maximum input length and short enough for your GPU RAM.

>>> block_size = 128


>>> def group_texts(examples):
...     # Concatenate all texts.
...     concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
...     total_length = len(concatenated_examples[list(examples.keys())[0]])
...     # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
...     # customize this part to your needs.
...     if total_length >= block_size:
...         total_length = (total_length // block_size) * block_size
...     # Split by chunks of block_size.
...     result = {
...         k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
...         for k, t in concatenated_examples.items()
...     }
...     return result

Apply the group_texts function over the entire dataset:

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

Now create a batch of examples using DataCollatorForLanguageModeling. 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.

Pytorch

Hide Pytorch content

Use the end-of-sequence token as the padding token and specify mlm_probability to randomly mask tokens each time you iterate over the data:

>>> from transformers import DataCollatorForLanguageModeling

>>> tokenizer.pad_token = tokenizer.eos_token
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)

TensorFlow

Hide TensorFlow content

Use the end-of-sequence token as the padding token and specify mlm_probability to randomly mask tokens each time you iterate over the data:

>>> from transformers import DataCollatorForLanguageModeling

>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15, return_tensors="tf")

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 DistilRoBERTa with AutoModelForMaskedLM:

>>> from transformers import AutoModelForMaskedLM

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

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

>>> training_args = TrainingArguments(
...     output_dir="my_awesome_eli5_mlm_model",
...     evaluation_strategy="epoch",
...     learning_rate=2e-5,
...     num_train_epochs=3,
...     weight_decay=0.01,
...     push_to_hub=True,
... )

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

>>> trainer.train()

Once training is completed, use the evaluate() method to evaluate your model and get its perplexity:

>>> import math

>>> eval_results = trainer.evaluate()
>>> print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
Perplexity: 8.76

Then 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, AdamWeightDecay

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

Then you can load DistilRoBERTa with TFAutoModelForMaskedLM:

>>> from transformers import TFAutoModelForMaskedLM

>>> model = TFAutoModelForMaskedLM.from_pretrained("distilroberta-base")

Convert your datasets to the tf.data.Dataset format with prepare_tf_dataset():

>>> tf_train_set = model.prepare_tf_dataset(
...     lm_dataset["train"],
...     shuffle=True,
...     batch_size=16,
...     collate_fn=data_collator,
... )

>>> tf_test_set = model.prepare_tf_dataset(
...     lm_dataset["test"],
...     shuffle=False,
...     batch_size=16,
...     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:

>>> import tensorflow as tf

>>> model.compile(optimizer=optimizer)  # No loss argument!

This can be done by specifying where to push your model and tokenizer in the PushToHubCallback:

>>> from transformers.keras_callbacks import PushToHubCallback

>>> callback = PushToHubCallback(
...     output_dir="my_awesome_eli5_mlm_model",
...     tokenizer=tokenizer,
... )

Finally, you’re ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callback to finetune the model:

>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])

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 masked language modeling, 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 you’d like the model to fill in the blank with, and use the special <mask> token to indicate the blank:

>>> text = "The Milky Way is a <mask> galaxy."

The simplest way to try out your finetuned model for inference is to use it in a pipeline(). Instantiate a pipeline for fill-mask with your model, and pass your text to it. If you like, you can use the top_k parameter to specify how many predictions to return:

>>> from transformers import pipeline

>>> mask_filler = pipeline("fill-mask", "stevhliu/my_awesome_eli5_mlm_model")
>>> mask_filler(text, top_k=3)
[{'score': 0.5150994658470154,
  'token': 21300,
  'token_str': ' spiral',
  'sequence': 'The Milky Way is a spiral galaxy.'},
 {'score': 0.07087188959121704,
  'token': 2232,
  'token_str': ' massive',
  'sequence': 'The Milky Way is a massive galaxy.'},
 {'score': 0.06434620916843414,
  'token': 650,
  'token_str': ' small',
  'sequence': 'The Milky Way is a small galaxy.'}]

Pytorch

Hide Pytorch content

Tokenize the text and return the input_ids as PyTorch tensors. You’ll also need to specify the position of the <mask> token:

>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
>>> inputs = tokenizer(text, return_tensors="pt")
>>> mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]

Pass your inputs to the model and return the logits of the masked token:

>>> from transformers import AutoModelForMaskedLM

>>> model = AutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
>>> logits = model(**inputs).logits
>>> mask_token_logits = logits[0, mask_token_index, :]

Then return the three masked tokens with the highest probability and print them out:

>>> top_3_tokens = torch.topk(mask_token_logits, 3, dim=1).indices[0].tolist()

>>> for token in top_3_tokens:
...     print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
The Milky Way is a spiral galaxy.
The Milky Way is a massive galaxy.
The Milky Way is a small galaxy.

TensorFlow

Hide TensorFlow content

Tokenize the text and return the input_ids as TensorFlow tensors. You’ll also need to specify the position of the <mask> token:

>>> from transformers import AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
>>> inputs = tokenizer(text, return_tensors="tf")
>>> mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]

Pass your inputs to the model and return the logits of the masked token:

>>> from transformers import TFAutoModelForMaskedLM

>>> model = TFAutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
>>> logits = model(**inputs).logits
>>> mask_token_logits = logits[0, mask_token_index, :]

Then return the three masked tokens with the highest probability and print them out:

>>> top_3_tokens = tf.math.top_k(mask_token_logits, 3).indices.numpy()

>>> for token in top_3_tokens:
...     print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
The Milky Way is a spiral galaxy.
The Milky Way is a massive galaxy.
The Milky Way is a small galaxy.

←Causal language modeling Translation→