guillaumegenthial/sequence_tagging

Health v2 · maintenance only

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1952 stars · 696 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A TensorFlow implementation of a Bi-LSTM + CRF model with character embeddings for named entity recognition and sequence tagging. It trains on CoNLL-format data and achieves near state-of-the-art F1 scores (~90-91).

Use cases

• train a named entity recognition model on CoNLL-2003 data
• tag each word in a sentence with entity labels like PER, LOC, ORG
• learn how to implement LSTM-CRF sequence tagging in TensorFlow
• use GloVe word vectors plus character embeddings for NER
• run a sequence labeling model on custom IOB-formatted datasets
• reproduce results from Lample et al. and Ma and Hovy NER papers

When to choose

• you need a clear, educational reference implementation of Bi-LSTM + CRF NER in TensorFlow 1.x
• you want to train a sequence tagger on CoNLL-format data with pretrained GloVe embeddings
• you are studying classic deep learning approaches to NER before transformer-based models

When to avoid

• you need a maintained, production-ready NER system - the repo points to a newer implementation (tf_ner) and sees little activity
• you want to use TensorFlow 2.x or modern tooling like tf.data and tf.estimator out of the box
• you need transformer-based NER with pretrained language models such as BERT

Facets

library · maturity maintenance

machine-learning nlp machine-learning deep-learning python named-entity-recognition ner sequence-labeling crf bi-lstm tensorflow word-embeddings glove character-embeddings natural-language-processing

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