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allenai/multinews_sparse_mean

This is a copy of the Multi-News dataset, except the input source documents of its test split have been replaced by a sparse retriever.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==3… See the full description on the dataset page: https://huggingface.co/datasets/allenai/multinews_sparse_mean.

sourceHugging Faceotherupdated 4y agoView on Hugging Face

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This is a copy of the Multi-News dataset, except the input source documents of its test split have been replaced by a _sparse_ retriever.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==3

Retrieval results on the train set:

Recall@100	Rprec	Precision@k	Recall@k
0.8793	0.7460	0.6403	0.7417

Retrieval results on the validation set:

Recall@100	Rprec	Precision@k	Recall@k
0.8748	0.7453	0.6361	0.7442

Retrieval results on the test set:

Recall@100	Rprec	Precision@k	Recall@k
0.8775	0.7480	0.6370	0.7443