Natural Language Processing (NLP) is a rapidly growing field of Artificial Intelligence (AI) that focuses on the interaction between humans and computers using natural language. One of the most common tasks in NLP is fill-in-the-blank, where the goal is to predict missing words in a sentence. Fill-in-the-blank tasks have many applications in NLP, such as language modeling, question answering, sentiment analysis, and named entity recognition. Hugging Face Transformers is a popular Python library that provides pre-trained NLP models, including BERT, GPT and many others, and allows fine-tuning them on custom tasks. In this tutorial, we will explore how to use Hugging Face Transformers and BERT for fill-in-the-blank tasks in PyTorch. We will go step-by-step through the process of loading a pre-trained BERT model, tokenizing text, replacing a word with a [MASK] token, feeding the tokenized text into the BERT model, and obtaining the predicted word. By the end of this tutorial, you will have a solid understanding of how to use Hugging Face Transformers and BERT for fill-in-the-blank tasks in NLP.
First, you will need to install the transformers library by running:
pip install transformers
Once the library is installed, you can import the necessary modules:
import torch
from transformers import BertForMaskedLM, BertTokenizer
Next, you will need to load the pre-trained BERT model and tokenizer:
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
Now, let’s say you have a sentence that has a missing word that you want BERT to predict. Here’s an example sentence:
sentence = "The quick brown [MASK] jumps over the lazy dog"
To use BERT to predict the missing word, you first need to tokenize the sentence and replace the missing word with the [MASK] token:
tokenized_text = tokenizer.tokenize(sentence)
masked_index = tokenized_text.index('[MASK]')
tokenized_text[masked_index] = '[MASK]'
Now, you can convert the tokenized text into input features that can be fed into the BERT model:
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
Finally, you can pass the input features through the BERT model to obtain a prediction:
with torch.no_grad():
outputs = model(tokens_tensor)
predictions = outputs[0][0, masked_index].topk(5).indices.tolist()
In this example, we obtain the top 5 predicted tokens for the missing word using the topk method.
You can then convert the predicted token IDs back to their corresponding words:
predicted_tokens = tokenizer.convert_ids_to_tokens(predictions)
print(predicted_tokens)
This will output a list of the top 5 predicted tokens for the missing word.
And that’t it!
