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:

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

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:

Load ELI5 dataset

Start by loading the first 5000 examples from the ELI5-Category dataset with 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.

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

Then take a look at an example:

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:

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

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:

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:

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.

Apply the group_texts function over the entire dataset:

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.

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:

Train

You're ready to start training your model now! Load DistilRoBERTa with AutoModelForMaskedLM:

At this point, only three steps remain:

1. 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).
2. Pass the training arguments to Trainer along with the model, datasets, and data collator.
3. Call train() to finetune your model.

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

Then share your model to the Hub with the push_to_hub() method so everyone can use your model:

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:

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:

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

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

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