This Python script creates a class chunk4096.py utilizing the Gradio library, PyTorch, and the Transformers library to perform text chunking and natural language generation. It defines a Chunker class responsible for breaking down input text into chunks and generating a response for each chunk. The chunk size is set to 4096 tokens.
Gradio
PyTorch
Transformers
Constants and Variables
BOS, EOS: Constants for Beginning Of Sentence and End Of Sentence tokens.
B_INST, E_INST: Constants for beginning and ending an instance in the text.
B_SYS, E_SYS: Constants for system-specific tokens.
Chunker
Class Attributes:
CHUNK_SIZE: Defines the chunk size for the class. Set to 4096 tokens.
Instance Attributes:
tokenizer: Tokenizer object for text tokenization.
model: Language model object for text generation.
chunk_and_generate(chunk_text):
Input: A string chunk_text.
Output: A generated string based on chunk_text.
Description: Tokenizes the input chunk and generates a text using the model.
Functions
chunk_and_generate_response(chunker, text)
Input:
chunker: An instance of the Chunker class.
text: A string containing the text to be chunked and processed.
Output: A string containing the generated text.
Splits the input text into chunks and utilizes chunker to generate text for each chunk. The generated text is then concatenated and returned.
Input Validation: Ensure that the chunked text is sanitized and valid.
Sensitive Data Protection: No sensitive data is handled in this script.
Least Privilege Principle: The script only uses the resources it needs.
Error Handling and Logging: Error handling can be implemented where the model or tokenizer is used to generate the text.