dslim/bert-base-NER

The model is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task . It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC) The training dataset was derived from the Reuters corpus which consists of Reuters news stories . You can use this model with Transformers pipeline for NER . The test metrics are a little lower than the official Google BERT results which encoded document context & experimented with CRF . More on replicating the original entry entry@DBLP:journals/corr/cor,