English NER in Flair (Ontonotes large model) This is the large 18 class NER model for English that ships with Flair. F1 Score: 90.93 (Ontonotes) Predicts 18 tags: tag meaning CARDINAL cardinal value DATE date value EVENT event name FAC building name GPE geo political entity LANGUAGE language name LAW law name LOC location name MONEY money name NORP affiliation ORDINAL ordinal value ORG organization name PERCENT percent value PERSON person name PRODUCT product name QUANTITY quantity value TIME time value WORK OF ART name of work of art 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 " September 1st " (labeled as a date ), " George " (labeled as a person ), " 1 dollar " (labeled as a money ) and "Game of Thrones" (labeled as a work of art ) are found in the sentence " On September 1st George Washington won 1 dollar while watching Game of Thrones ". 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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