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.
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.