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allenai/wcep_sparse_max

This is a copy of the WCEP-10 dataset, except the input source documents of its test split have been replaced by a sparse retriever.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: "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==10… See the full description on the dataset page: https://huggingface.co/datasets/allenai/wcep_sparse_max.

sourceHugging Faceotherupdated 4y agoView on Hugging Face

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This is a copy of the WCEP-10 dataset, except the input source documents of its test split have been replaced by a _sparse_ retriever.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_: "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==10

Retrieval results on the train set:

Recall@100	Rprec	Precision@k	Recall@k
0.8753	0.6443	0.5919	0.6588

Retrieval results on the validation set:

Recall@100	Rprec	Precision@k	Recall@k
0.8706	0.6280	0.5988	0.6346

Retrieval results on the test set:

Recall@100	Rprec	Precision@k	Recall@k
0.8836	0.6658	0.6296	0.6746