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**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.*