tanaos NER v1: A small but performant Named Entity Recognition model This model was created by Tanaos with the Artifex Python library. This is a Named Entity Recognition model based on FacebookAI/roberta base and fine tuned on a synthetic dataset to recognize and classify entities in text into the following 14 entity categories: Entity Description PERSON Individual people, fictional characters ORG Companies, institutions, agencies LOCATION Geographical areas DATE Absolute or relative dates, including years, months and/or days TIME Specific time of the day PERCENT Percentage expressions NUMBER Numeric measurements or expressions FACILITY Buildings, airports, highways, etc. PRODUCT Objects, vehicles, food, etc. bearing a specific name WORK OF ART Titles of creative works LANGUAGE Natural or programming languages NORP National, religious or political groups ADDRESS Full addresses PHONE NUMBER Telephone numbers These entities were chosen to cover a wide range of common named entity types that are useful in various NLP applications, regardless of the specific application domain, in order to create a versatile and general purpose Named Entity Recognition model, applicable across various…
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