embedding-benchmark/DS1000
DS-1000 is a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas. It employs multi-criteria evaluation metrics, including functional correctness and surface-form constraints, resulting in a high-quality dataset with only 1.8% incorrect solutions among accepted Codex-002 predictions. Usage import datasets # Download the dataset queries = datasets.load_dataset("embedding-benchmark/DS1000", "queries") documents =… See the full description on the dataset page: https://huggingface.co/datasets/embedding-benchmark/DS1000.
| Author / organization | embedding-benchmark |
|---|---|
| Downloads | 435 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 2 May 2025 |
| Last updated at source | 4 August 2025 |
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