Skip to content

Module Name: LanguageTokenizerGPTX

The LanguageTokenizerGPTX is an embedding utility tailored for the "EleutherAI/gpt-neox-20b" transformer model. This class allows for seamless tokenization and decoding operations, abstracting away the underlying complexity of the chosen transformer's tokenizer.

Introduction:

Language tokenization is a crucial step in natural language processing tasks. This module provides an interface to tokenize and decode text using the GPT-Neox-20b transformer from the EleutherAI project. With the ability to manage end-of-string tokens, padding tokens, and a fixed model length, LanguageTokenizerGPTX serves as a convenient wrapper for the actual tokenizer from the transformers library.

Class Definition:

class LanguageTokenizerGPTX:
    def __init__(self): ...
    def tokenize_texts(self, texts: str) -> torch.Tensor: ...
    def decode(self, texts: torch.Tensor) -> str: ...
    def __len__(self) -> int: ...

Parameters:

The class does not take any parameters upon instantiation. It uses predefined parameters internally to load the tokenizer.

Methods:

1. __init__(self) -> None:

Initializes the LanguageTokenizerGPTX object. This method loads the AutoTokenizer with predefined parameters.

2. tokenize_texts(self, texts: str) -> torch.Tensor:

Tokenizes a given text or list of texts.

  • texts (str): The input text(s) to tokenize.

Returns: - A torch Tensor of token IDs representing the input text(s).

3. decode(self, texts: torch.Tensor) -> str:

Decodes a given tensor of token IDs back to text.

  • texts (torch.Tensor): The tensor of token IDs to decode.

Returns: - A string representing the decoded text.

4. __len__(self) -> int:

Provides the total number of tokens in the tokenizer's vocabulary.

Returns: - An integer representing the total number of tokens.

Usage Examples:

import torch

from zeta import LanguageTokenizerGPTX

# Initialize the tokenizer
tokenizer = LanguageTokenizerGPTX()

# Example 1: Tokenize a single text
text = "Hello, world!"
tokenized_text = tokenizer.tokenize_texts(text)
print(tokenized_text)

# Example 2: Decode a tokenized text
decoded_text = tokenizer.decode(tokenized_text)
print(decoded_text)

# Example 3: Get the number of tokens in the tokenizer's vocabulary
num_tokens = len(tokenizer)
print(f"The tokenizer has {num_tokens} tokens.")

Mathematical Formulation:

Given a text ( t ) and a vocabulary ( V ) from the GPT-Neox-20b model, tokenization maps ( t ) to a sequence of token IDs ( T ) where each token ID ( t_i ) corresponds to a token in ( V ). Decoding reverses this process.

[ t \xrightarrow{\text{tokenize}} T ] [ T \xrightarrow{\text{decode}} t ]

Additional Information:

The GPT-Neox-20b model is part of the EleutherAI project. It's a variant of the GPT architecture with tweaks in terms of model size and training. Utilizing such models require an understanding of tokenization and decoding, which this module aims to simplify.

References:

Note: Ensure you have the necessary packages and dependencies installed, particularly the transformers library from Hugging Face.