|
|
--- |
|
|
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 |
|
|
``` |
|
|
|
