| | --- |
| | language: |
| | - en |
| | license: other |
| | tags: |
| | - code |
| | - seeds |
| | - oss-instruct |
| | - dataset-curation |
| | --- |
| | |
| | # oss-code-seeds |
| |
|
| | A combined dataset of open-source code snippets (seeds) curated from multiple |
| | high-quality sources. The intended use is to serve as seeds for generating |
| | coding instruction-response pairs via the OSS-Instruct approach — where a model |
| | is prompted with a real code snippet to produce a grounded, diverse coding problem |
| | and its solution. |
| |
|
| | ## Columns |
| |
|
| | - `seed`: A raw code snippet from open-source software (OSS), serving as the seed |
| | - `source`: The original HuggingFace dataset the seed was taken from |
| |
|
| | ## Sources |
| |
|
| | | Dataset | Description | |
| | |---|---| |
| | | `ise-uiuc/Magicoder-OSS-Instruct-75K` | Seeds used to generate the Magicoder dataset via GPT-3.5, multi-language GitHub snippets | |
| | | `bigcode/self-oss-instruct-sc2-concepts` | Filtered Python functions from The Stack V1 used in the SelfCodeAlign pipeline | |
| |
|
| | ## Intended Use |
| |
|
| | Feed the `seed` column into your own model API to generate coding problems and |
| | solutions, effectively replicating or improving upon the OSS-Instruct pipeline |
| | with your own model. |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | |
| | ds = load_dataset("PursuitOfDataScience/oss-code-seeds", split="train") |
| | |
| | for row in ds: |
| | seed = row["seed"] |
| | source = row["source"] |
| | # prompt your model with seed to generate a problem + solution |
| | ``` |
| |
|
