kaiyinzhou/BERT-NER

Health v2 · maintenance only

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Adoption not part of the score

1277 stars · 325 forks observed · 2026-08-28

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

A Python implementation that fine-tunes Google's BERT for named entity recognition using the CoNLL-2003 dataset, built on TensorFlow. It provides training, evaluation, and prediction scripts with optional CRF layer support for sequence labeling.

Use cases

• train a named entity recognition model with BERT
• fine-tune BERT on CoNLL-2003 NER data
• recognize person, location, and organization names in text
• compare CRF vs softmax output layers for NER
• reproduce BERT NER benchmark results
• learn how to fine-tune BERT for sequence labeling

When to choose

• you need a straightforward BERT-based NER implementation in TensorFlow
• you want a reference implementation for fine-tuning BERT on sequence labeling tasks
• you are working with the CoNLL-2003 dataset or a similar NER dataset

When to avoid

• you need a maintained or production-ready NLP library
• you prefer PyTorch or modern transformer frameworks like Hugging Face
• you need multi-language or custom-domain NER out of the box
• you want the latest NLP model architectures beyond BERT

Facets

library · maturity maintenance

machine-learning nlp deep-learning machine-learning deep-learning python cross-platform bert named-entity-recognition ner tensorflow conll-2003 fine-tuning sequence-labeling natural-language-processing linux

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