---
title: multixscience_sparse_oracle
canonical_url: "https://www.modelscope.cn/datasets/allenai/multixscience_sparse_oracle"
md_url: "https://www.modelscope.cn/datasets/allenai/multixscience_sparse_oracle.md"
repository: allenai/multixscience_sparse_oracle
last_updated: 2025-05-27
license: "Apache License 2.0"
storage_size: "97 MB"
downloads: 592
stars: 0
---

# multixscience_sparse_oracle

> multixscience_sparse_oracle - allenai 在 ModelScope 开源的数据集。This is a copy of the Multi-XScience dataset, except the input source documents of its test split have been replaced by a sparse retriever. The retrieval pipeline used:

allenai/multixscience_sparse_oracle 是 ModelScope 魔搭社区上的数据集，存储大小 97 MB，采用 Apache License 2.0 许可。

- **Repository**: allenai/multixscience_sparse_oracle
- **License**: Apache License 2.0
- **Storage size**: 97 MB
- **Downloads**: 592
- **Stars**: 0
- **Last updated**: 2025-05-27

Source: https://www.modelscope.cn/datasets/allenai/multixscience_sparse_oracle

---

This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:

- __query__: The `related_work` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example

Retrieval results on the `train` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5482 | 0.2243 | 0.2243 | 0.2243 |

Retrieval results on the `validation` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5476 | 0.2209 | 0.2209 | 0.2209 |

Retrieval results on the `test` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5480 | 0.2272 | 0.2272 | 0.2272 |