guillaumegenthial/tf_ner
TensorFlow implementation of Bidirectional LSTM-CRF neural networks for Named Entity Recognition sequence labeling.
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Provides simple and efficient TensorFlow implementations of neural-network-based NER models using tf.estimator and tf.data APIs. The models achieve state-of-the-art results on standard NER benchmarks using BiLSTM-CRF architecture with character embeddings and GloVe word embeddings. Supports the CoNLL-2003 dataset format for training and evaluation.
Frequently asked
- What is guillaumegenthial/tf_ner?
- TensorFlow implementation of Bidirectional LSTM-CRF neural networks for Named Entity Recognition sequence labeling.
- Is tf_ner open source?
- Yes — guillaumegenthial/tf_ner is open source, released under the Apache-2.0 license.
- What language is tf_ner written in?
- guillaumegenthial/tf_ner is primarily written in Python.
- How popular is tf_ner?
- guillaumegenthial/tf_ner has 924 stars on GitHub.
- Where can I find tf_ner?
- guillaumegenthial/tf_ner is on GitHub at https://github.com/guillaumegenthial/tf_ner.