{}
Model Card: (TEST) code-search-net-tokenizer
Model Description:
The Code Search Net Tokenizer is a custom tokenizer specifically trained for tokenizing Python code snippets. It has been trained on a large corpus of Python code snippets from the CodeSearchNet dataset using the GPT-2 model as a starting point. The goal of this tokenizer is to effectively tokenize Python code for use in various natural language processing and code-related tasks.
Model Details:
Name: Code Search Net Tokenizer Model Type: Custom Tokenizer Language: Python
Training Data:
The tokenizer was trained on a corpus of Python code snippets from the CodeSearchNet dataset. The dataset consists of various Python code examples collected from open-source repositories on GitHub. The tokenizer has been fine-tuned on this dataset to create a specialized vocabulary that captures the unique syntax and structure of Python code.
Tokenizer Features:
*The Code Search Net Tokenizer offers the following features:
*Tokenization of Python code: The tokenizer can effectively split Python code snippets into individual tokens, making it suitable for downstream tasks that involve code processing and understanding.
Usage:
You can use the code-search-net-tokenizer to preprocess code snippets and convert them into numerical representations suitable for feeding into language models like GPT-2, BERT, or RoBERTa.
Limitations:
The code-search-net-tokenizer is specifically tailored to code-related text data and may not be suitable for general text tasks. It may not perform optimally for natural language text outside the programming context.
This model card is provided for informational purposes only and does not guarantee specific performance or outcomes when using the "code-search-net-tokenizer" with other language models. Users are encouraged to refer to the Hugging Face documentation and model repository for detailed information and updates.