guillaumegenthial/tf_ner
Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
This project helps natural language processing engineers quickly implement and test state-of-the-art Named Entity Recognition (NER) models. You provide text sentences and corresponding tags, and the system outputs a trained model that can identify entities like names, locations, and organizations in new text. It is designed for NLP researchers and practitioners who need accurate and efficient entity extraction.
926 stars. No commits in the last 6 months.
Use this if you are an NLP developer or researcher looking for a simple, accurate, and efficient framework to build and evaluate custom Named Entity Recognition models.
Not ideal if you are a non-technical user needing an out-of-the-box solution for entity extraction without custom development or model training.
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Python
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Apache-2.0
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Last pushed
Dec 18, 2018
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