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allenai/multixscience_sparse_oracle

This is a copy of the Multi-XScience dataset, except the input source documents of its test split have been replaced by a sparse retriever.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: BM25 via PyTerrier with default settings top-k strategy: "oracle", i.e. the number of documents retrieved, k, is set as the original number of input documents for each example Retrieval results… See the full description on the dataset page: https://huggingface.co/datasets/allenai/multixscience_sparse_oracle.

sourceHugging Faceunknownupdated 4y agoView on Hugging Face

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This is a copy of the Multi-XScience dataset, except the input source documents of its test split have been replaced by a _sparse_ retriever. The retrieval pipeline used:

—_query: The `relatedwork` 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_: "oracle", i.e. the number of documents retrieved, k, is set as the original number of input documents for each example

Retrieval results on the train set:

Recall@100	Rprec	Precision@k	Recall@k
0.5482	0.2243	0.2243	0.2243

Retrieval results on the validation set:

Recall@100	Rprec	Precision@k	Recall@k
0.5476	0.2209	0.2209	0.2209

Retrieval results on the test set:

Recall@100	Rprec	Precision@k	Recall@k
0.5480	0.2272	0.2272	0.2272