Hugging Face hosts Computer Code Datasets

As of my last update in January 2022, Hugging Face indeed hosts a variety of datasets, libraries, and models related to natural language processing (NLP) and machine learning. While Hugging Face primarily focuses on NLP, it does have datasets that contain computer code data or are useful for tasks involving code.

Here are a few datasets on Hugging Face that might be relevant for tasks involving computer code data:

1. CodeSearchNet: This dataset contains a large collection of code snippets from open-source repositories. It includes metadata such as the repository name, the programming language, and the function name.

2. CodeXGLUE: CodeXGLUE is a collection of datasets and evaluation benchmarks for the code-related tasks, including code completion, code summarization, and code-to-text generation.

3. CodeSearchNet Challenge: This dataset is a part of the CodeSearchNet project and includes a set of tasks for code retrieval and code summarization.

4. github-python: This dataset contains Python code snippets collected from GitHub repositories. It can be used for tasks such as code summarization, code search, or code generation.

5. github_csharp: Similar to the github-python dataset, this contains C# code snippets collected from GitHub repositories.

GitHub allows some access to its code for LLM (Large Language Model) creation in a few ways:

1. Public Repositories:

• Code Search: GitHub offers code search functionality, allowing researchers and developers to find relevant code snippets within public repositories. This can be valuable for training LLMs on specific programming languages and coding patterns.

• Open Source Projects: Many open-source projects on GitHub provide their codebases freely. LLMs can be trained on this extensive collection of code to learn programming concepts, syntax, and problem-solving techniques.

2. GitHub API:

• The GitHub API allows developers to interact with GitHub repositories programmatically. LLMs can be trained to utilize this API to:

• Retrieve code from specific repositories

• Analyze code structure and relationships

• Understand code comments and documentation

3. Partnerships and Data Licensing

• GitHub may enter into partnerships with organizations or researchers interested in utilizing large-scale code data for LLM development. These partnerships could involve special access to private repositories or the creation of curated datasets.

• Some code on GitHub might be available through licensing agreements specifically designed for machine learning purposes.

Important Considerations:

• Privacy and Licensing: It's crucial to respect the privacy settings and licensing terms of any code used for LLM creation. Using code without proper permission can be a violation of copyright.

• Code Quality: Not all code on GitHub is of high quality. LLMs need to be trained on well-written, well-documented code to avoid learning bad coding practices.

• Data Filtering: Large-scale code datasets often require careful filtering and cleaning to remove irrelevant, low-quality, or potentially harmful code.

These datasets can be useful for training models for various tasks related to code understanding, code generation, and code search. You can explore these datasets further on the Hugging Face website or through their API. Additionally, new datasets might have been added since my last update, so it's a good idea to check their website for the most up-to-date information.