wcep_sparse_mean

This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:

- __query__: The `summary` 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__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==9`

Retrieval results on the `train` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8753 | 0.6443 | 0.6196 | 0.6237 |

Retrieval results on the `validation` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8706 | 0.6280 | 0.6260 | 0.5989 |

Retrieval results on the `test` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8836 | 0.6658 | 0.6601 | 0.6388 |

本数据集为[WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10)的复刻版本，仅将其`test`划分的输入源文档替换为**稀疏检索器（sparse retriever）**的检索结果。所用检索流程如下：
- **查询（query）**：每个样本的`summary`字段
- **语料库（corpus）**：`train`、`validation`及`test`划分下所有文档的并集
- **检索器（retriever）**：基于[PyTerrier](https://pyterrier.readthedocs.io/en/latest/)实现的BM25检索器，采用默认参数配置
- **Top-k策略（top-k strategy）**：采用"mean"（均值）策略，即检索文档数`k`设置为当前数据集所有样本中实际出现的文档数的平均值，本次任务中`k=9`

训练集检索结果：
| 召回率@100（Recall@100） | R预精度（R-precision） | 精准率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.8753 | 0.6443 | 0.6196 | 0.6237 |

验证集检索结果：
| 召回率@100（Recall@100） | R预精度（R-precision） | 精准率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.8706 | 0.6280 | 0.6260 | 0.5989 |

测试集检索结果：
| 召回率@100（Recall@100） | R预精度（R-precision） | 精准率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.8836 | 0.6658 | 0.6601 | 0.6388 |