What is Causal Language Modeling in transformers?

This recipe explains what is Causal Language Modeling in transformers.

Recipe Objective - What is Causal Language Modeling in transformers?

The task of fitting a model to a corpus, which can be domain-specific, is known as language modeling. Language modeling versions, such as BERT with masked language modeling and GPT2 with causal language modeling, are used to train all popular transformers-based models.

Language modeling is also useful outside of pre-training, for example, to transform the model distribution in a specific domain: use a trained language model on a very large corpus and then fit it to data sets from news or scientific articles, such as LysandreJik / arxivnlp.

Learn How to Build a Multi Class Text Classification Model using BERT

Causal Language Modeling:

The task of predicting the token after a sequence of tokens is known as causal language modeling. In this case, the model is just concerned with the left context (tokens on the left of the mask).

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Example of Causal Language Model using pipeline:

# Importing libraries
from transformers import AutoModelWithLMHead, AutoTokenizer, top_k_top_p_filtering
import torch
from torch import nn

# Creating tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("gpt2")

# Creating context for sequence
context_sequence = f"I have never watched anything like this, and it was"

# Applying tokenizer on sequence
tokens = tokenizer.encode(context_sequence, return_tensors="pt")

# Extracting logits of last hidden state
last_logits = model(tokens).logits[:, -1, :]

# Applying top k top p filtering
filter = top_k_top_p_filtering(last_logits, top_k=50, top_p=1.0)

# Finding probabilities using softmax function
probabilities = nn.functional.softmax(filter, dim=-1)

# Applying multinomial
final_token = torch.multinomial(probabilities, num_samples=1)

# Applying cat function
output = torch.cat([tokens, final_token], dim=-1)

# Decoding
answer = tokenizer.decode(output.tolist()[0])

# Printing answer
print(answer)

Output -
I have never watched anything like this, and it was amazing

In this way, we can perform causal language modeling in transformers.

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