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数据集:
allenai/wcep_sparse_mean
任务:
摘要生成
语言:
en
计算机处理:
monolingual大小:
1K<n<10K语言创建人:
expert-generated批注创建人:
expert-generated源数据集:
original许可:
other
中文This is a copy of the WCEP-10 dataset, except the input source documents of its test split have been replaced by a sparse retriever.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 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 |