A fine-grained manually annotated named entity recognition dataset
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). The schema of Few-NERD is: Few-NERD is manually annotated based on the context, for example, in the sentence “London is the fifth album by the British rock band…“, the named entity London […]
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