Attention masks are basically just fancy ways of saying “hey, pay attention to this part over here but ignore that other part.” They help the transformer model focus on specific parts of an input sequence and disregard irrelevant information.
For example, let’s say you have a sentence like: “The quick brown fox jumps over the lazy dog” and you want your model to pay attention only to the words that are actually important for understanding the meaning (like “quick”, “brown”, and “jumps”). You can create an attention mask by setting all other positions to zero, effectively telling the model to ignore them.
Here’s what it might look like in code:
# Import the necessary libraries
import torch
from transformers import BertTokenizerFast
# Create a tokenizer using the BERT model
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
# Define a sentence to tokenize
sentence = "The quick brown fox jumps over the lazy dog"
# Tokenize the sentence using the tokenizer and return the input ids
input_ids = tokenizer(sentence, return_tensors='pt').input_ids
# Create an attention mask with all positions set to 1 (pay attention to everything)
attention_mask = torch.ones((1, input_ids.shape[1]), dtype=torch.long) # set all positions to 1 (pay attention to everything)
# Create an attention mask by setting all other positions to zero, effectively telling the model to ignore them
attention_mask = torch.zeros((1, input_ids.shape[1]), dtype=torch.long) # set all positions to 0 (ignore them)
Now position IDs. These are just a way of keeping track of where each word is in the sequence. They help the model understand which words come before or after others, and can be useful for tasks like predicting what comes next based on previous context.
For example, if you have a sentence like: “The quick brown fox jumps over the lazy dog” and you want to know where the word “jumps” is in relation to other words, you can use position IDs to find out that it’s at index 10 (since we start counting from zero).
Here’s what it might look like in code:
# Import the necessary libraries
import torch
from transformers import BertTokenizerFast
# Initialize the tokenizer using the pre-trained 'bert-base-uncased' model
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
# Define a sentence to tokenize
sentence = "The quick brown fox jumps over the lazy dog"
# Use the tokenizer to convert the sentence into input_ids, which is a list of token IDs
input_ids = tokenizer(sentence, return_tensors='pt').input_ids
# Create a sequence of numbers from 0 to n-1 (where n is the length of the input sequence)
position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long)
I hope that helps clarify things for you! Let me know if you have any other questions or if there’s anything else I can do to help.