ms2_sparse_max

This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:

- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==25`

Retrieval results on the `train` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4333 | 0.2163 | 0.1746 | 0.2636 |

Retrieval results on the `validation` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.378 | 0.1827 | 0.1559 | 0.2188 |

Retrieval results on the `test` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.3928 | 0.1898 | 0.1672 | 0.2208 |

本数据集为[MS^2](https://huggingface.co/datasets/allenai/mslr2022)的副本，仅将其`validation`（验证集）划分的输入源文档替换为**稀疏**检索器所获取的文档。所用检索流程如下：
- **查询（query）**：每个样本的`background`（背景）字段
- **语料库（corpus）**：`train`（训练集）、`validation`（验证集）与`test`（测试集）划分下的所有文档的并集。单篇文档由`title`（标题）与`abstract`（摘要）拼接而成。
- **检索器（retriever）**：基于[PyTerrier](https://pyterrier.readthedocs.io/en/latest/)实现的BM25检索模型，采用默认参数配置
- **Top-k策略**：采用`"max"`策略，即检索文档数`k`设为该数据集所有样本中观测到的最大文档数，本次场景下`k=25`。

训练集检索性能指标：
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4333 | 0.2163 | 0.1746 | 0.2636 |

验证集检索性能指标：
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.378 | 0.1827 | 0.1559 | 0.2188 |

测试集检索性能指标：
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.3928 | 0.1898 | 0.1672 | 0.2208 |