languidsheep/bc5cdr

--- language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: BioCreative V CDR --- # Dataset Card for "tner/bc5cdr" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true) - **Dataset:** BioCreative V CDR - **Domain:** Biomedical - **Number of Entity:** 2 ### Dataset Summary BioCreative V CDR NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size. - Entity Types: `Chemical`, `Disease` ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { 'tags': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], 'tokens': ['Fasciculations', 'in', 'six', 'areas', 'of', 'the', 'body', 'were', 'scored', 'from', '0', 'to', '3', 'and', 'summated', 'as', 'a', 'total', 'fasciculation', 'score', '.'] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/bc5cdr/raw/main/dataset/label.json). ```python { "O": 0, "B-Chemical": 1, "B-Disease": 2, "I-Disease": 3, "I-Chemical": 4 } ``` ### Data Splits | name |train|validation|test| |---------|----:|---------:|---:| |bc5cdr|5228| 5330|5865| ### Citation Information ``` @article{wei2016assessing, title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task}, author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong}, journal={Database}, volume={2016}, year={2016}, publisher={Oxford Academic} } ```language: - 英语（en） license: - 其他（other） multilinguality: - 单语（monolingual） size_categories: - 10K<n<100K task_categories: - 词元分类（token-classification） task_ids: - 命名实体识别（Named Entity Recognition） pretty_name: BioCreative V CDR --- # 「tner/bc5cdr」数据集卡片 ## 数据集描述 - **仓库**：[T-NER](https://github.com/asahi417/tner) - **论文**：[https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true) - **数据集**：BioCreative V CDR - **应用领域**：生物医学（Biomedical） - **实体类别数量**：2 ### 数据集概览 BioCreative V CDR命名实体识别（Named Entity Recognition）数据集适配[T-NER](https://github.com/asahi417/tner)项目格式。原始数据集包含过长文档，因篇幅限制无法直接输入至大语言模型（Large Language Model，LLM），故我们将其拆分为句子以缩减数据规模。 - 实体类型：`Chemical`（化学物质）、`Disease`（疾病） ## 数据集结构 ### 数据样例 训练集（train）的一条样例如下： { 'tags': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], 'tokens': ['Fasciculations', 'in', 'six', 'areas', 'of', 'the', 'body', 'were', 'scored', 'from', '0', 'to', '3', 'and', 'summated', 'as', 'a', 'total', 'fasciculation', 'score', '.'] } ### 标签映射 标签与ID的对应关系可参见[此处](https://huggingface.co/datasets/tner/bc5cdr/raw/main/dataset/label.json)： python { "O": 0, "B-Chemical": 1, "B-Disease": 2, "I-Disease": 3, "I-Chemical": 4 } ### 数据划分 | 数据集拆分 | 训练集 | 验证集 | 测试集 | |---------|----:|---------:|---:| | bc5cdr | 5228 | 5330 | 5865 | ### 引用信息 @article{wei2016assessing, title={评估生物医学关系抽取领域的当前技术水平：BioCreative V化学物质-疾病关系（CDR）任务概述}, author={魏智铉（Chih-Hsuan Wei）、彭一帆（Yifan Peng）、罗伯特·莱曼（Robert Leaman）、艾伦·彼得·戴维斯（Allan Peter Davis）、卡罗琳·J·马廷利（Carolyn J Mattingly）、李焦（Jiao Li）、托马斯·C·威格斯（Thomas C Wiegers）、卢志勇（Zhiyong Lu）}, journal={Database}, volume={2016}, year={2016}, publisher={牛津学术（Oxford Academic）} }