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BASF-AI/ChemNQRetrieval

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Hugging Face2024-09-23 更新2025-04-12 收录
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--- dataset_info: - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_bytes: 11814444.868325485 num_examples: 22933 download_size: 6046630 dataset_size: 11814444.868325485 - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: test num_bytes: 1110.7605332063795 num_examples: 35 download_size: 2196 dataset_size: 1110.7605332063795 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_bytes: 1724.433371958285 num_examples: 27 download_size: 2922 dataset_size: 1724.433371958285 configs: - config_name: corpus data_files: - split: corpus path: corpus/corpus-* - config_name: default data_files: - split: test path: data/test-* - config_name: queries data_files: - split: queries path: queries/queries-* task_categories: - question-answering language: - en tags: - chemistry - wikipedia - nq - natural questions - chemteb pretty_name: Chemical Natural Questions size_categories: - 10K<n<100K license: cc-by-nc-sa-4.0 --- # Chemical Natural Questions This dataset is created from the [mteb/nq](https://huggingface.co/datasets/mteb/nq) dataset on Hugging Face, which is part of the [Natural Questions](https://ai.google.com/research/NaturalQuestions/) dataset containing real user questions issued to Google search, with answers sourced from Wikipedia. In this chemistry-specific subset, we filtered queries related to chemistry by starting from the chemistry category in Wikipedia and traversing up to three levels deep in linked articles. This approach allowed us to focus on chemistry-related queries, providing a targeted subset of the original dataset for domain-specific retrieval tasks.
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