Few-NERD

Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)). Few-NERD is collected by researchers from Tsinghua University and DAMO Academy, Alibaba Group. <br />
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Website: https://ningding97.github.io/fewnerd/ <br />
Paper: https://arxiv.org/abs/2105.07464 <br />
Github: https://github.com/thunlp/Few-NERD <br />

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Chinese: Few-NERD是一个大规模，多粒度的人工标注命名实体识别（Named Entity Recognition, NER）数据集，包含了8个大类，66个小类，18万余个句子，49余万个实体。本数据集包括3个任务，分别为标准监督NER（Few-NERD (SUP)），跨大类Few-shot NER（Few-NERD (INTRA)）和不跨大类的Few-shot NER （Few-NERD (INTER)）。Few-NERD由清华大学和阿里巴巴的研究者构建而成。<br />
<br />
网址: https://ningding97.github.io/fewnerd/ <br />
论文: https://arxiv.org/abs/2105.07464 <br />
代码: https://github.com/thunlp/Few-NERD

Few-NERD是一个大规模、细粒度的人工标注命名实体识别（Named Entity Recognition, NER）数据集，涵盖8种粗粒度类别、66种细粒度类别，包含188200条语句、491711个实体以及4601223个Token。该数据集构建了三类基准评测任务：其中一项为监督学习任务（Few-NERD (SUP)），剩余两项为少样本学习任务，分别为Few-NERD (INTRA)与Few-NERD (INTER)。Few-NERD数据集由清华大学与阿里巴巴集团达摩院（DAMO Academy）的研究者构建采集。

网站：https://ningding97.github.io/fewnerd/
论文：https://arxiv.org/abs/2105.07464
Github：https://github.com/thunlp/Few-NERD

搜集汇总

背景与挑战

背景概述

Few-NERD是一个大规模、细粒度的人工标注命名实体识别数据集，包含8个粗粒度类型和66个细粒度类型，总计超过18.8万个句子和49万个实体。该数据集设计了三个基准任务，包括一个监督学习任务和两个少样本学习任务，旨在全面评估模型在标准命名实体识别和少样本场景下的性能，适用于推动NER领域的研究和应用。