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dslim/bert-base-NER

The bert-base-NER model is a fine-tuned BERT model that achieves state-of-the-art performance on the CoNLL-2003 Named Entity Recognition task. It was trained on the English version of the standard CoNLL-2003 dataset and recognizes four types of entities: location, organization, person, and miscellaneous. The model occasionally tags subword tokens as entities and post-processing of results may be necessary to handle these cases.

The bert-base-NER model is a fine-tuned BERT model that achieves state-of-the-art performance on the CoNLL-2003 Named Entity Recognition task. It was trained on the English version of the standard CoNLL-2003 dataset and recognizes four types of entities: location, organization, person, and miscellaneous. The model occasionally tags subword tokens as entities and post-processing of results may be necessary to handle these cases.

Public
$0.0005 / sec
demoapi

f7c2808a659015eeb8828f3f809a2f1be67a2446

2023-03-03T06:32:17+00:00