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The bert-base-NER-uncased model is a token classification model that is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture. It is trained for Named Entity Recognition (NER) tasks, which involve identifying and classifying named entities in text, such as persons, organizations, locations, and other specified entities. The model is trained on a large corpus of text and can accurately predict the type of named entities in unseen text.

Dslim

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bert-large-NER

bert-base-NER

bert-base-NER-uncased

bert-base-NER-uncased

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