English NER in Flair (large model) This is the large 4 class NER model for English that ships with Flair. F1 Score: 94,36 (corrected CoNLL 03) Predicts 4 tags: tag meaning PER person name LOC location name ORG organization name MISC other name Based on document level XLM R embeddings and FLERT. Demo: How to use in Flair Requires: Flair ( pip install flair ) This yields the following output: So, the entities " George Washington " (labeled as a person ) and " Washington " (labeled as a location ) are found in the sentence " George Washington went to Washington ". Training: Script to train this model The following Flair script was used to train this model: Cite Please cite the following paper when using this model. Issues? The Flair issue tracker is available here.
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