--- license: apache-2.0 task_categories: - text-classification - feature-extraction language: - en pretty_name: >- This dataset contains GitHub issues (and their comments) collected from the [huggingface/datasets](https://github.com/huggingface/datasets) public repository. It was created as part of an NLP pipeline exercise to demonstrate data collection, augmentation, semantic search, and transformers inference. tags: - github - issues - nlp - text-classification - semantic-search - huggingface size_categories: - 1K Data Collection Process
1. Data Source: Publicly available records fetched from the GitHub REST API.
2. Collection Method: Using Python-based "requests" library to fetch issue titles, descriptions and metadata.
3. Augmentation: The raw data was transformed into a structured format and saved locally as a .jsonl file before being uploaded to Hugging Face.
4. Tools Used: Use Pandas to manipulate the dataset, drop irrelevant data rows, and use datasets for final repository hosting.

Compliance and policy alignment

1. Data Access and Classification Policy: The dataset is classified as "Public". This data is approved for public release as its disclosure causes no harm to the organization.
2. AI Use Policy: The use of the sentence-transformers model for indexing is "Low-Risk" AI use cases. It focuses on information retrieval rather than autlmated decision-making. Risks

1. Data Breach - Low: There aren't personal information in the issues, also only few relevant columns are kept during data cleaning stage.
2. Model Bias - Medium: There is only one repo used in this dataset, which is very limited. Should have included multiple repo resources in the future.