multixscience_dense_mean

This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `train`, `validation` and `test` splits have been replaced by a __dense__ retriever.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__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) 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==4`

Retrieval results on the `train` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5270 | 0.2005 | 0.1551 | 0.2357 |

Retrieval results on the `validation` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5310 | 0.2026 | 0.1603 | 0.2432 |

Retrieval results on the `test` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5229 | 0.2081 | 0.1612 | 0.2440 |

本数据集为[Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum)的副本，仅将其`train`（训练集）、`validation`（验证集）与`test`（测试集）划分的输入源文档替换为**密集型检索器（dense retriever）**。所采用的检索流程如下：
- 查询（query）：每个示例的`related_work`字段
- 语料库（corpus）：`train`、`validation`和`test`划分中所有文档的并集
- 检索器（retriever）：通过[PyTerrier](https://pyterrier.readthedocs.io/en/latest/)采用默认配置运行的[facebook/contriever-msmarco](https://huggingface.co/facebook/contriever-msmarco)
- Top-K策略（top-k strategy）：`"max"`，即检索文档的数量`k`被设置为该数据集中所有示例所需的最大文档数，本次场景下`k=4`

训练集检索结果：
| 召回率@100（Recall@100） | R精度（R-precision） | 检索精度@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.5270 | 0.2005 | 0.1551 | 0.2357 |

验证集检索结果：
| 召回率@100（Recall@100） | R精度（R-precision） | 检索精度@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.5310 | 0.2026 | 0.1603 | 0.2432 |

测试集检索结果：
| 召回率@100（Recall@100） | R精度（R-precision） | 检索精度@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.5229 | 0.2081 | 0.1612 | 0.2440 |