Hironsan/anago

Health v2 · maintenance only

Adoption not part of the score

1482 stars · 359 forks observed · 2026-08-28

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

anaGo is a Python library for sequence labeling tasks such as named entity recognition, part-of-speech tagging, and semantic role labeling, built on Keras with BiLSTM-CRF and ELMo support. It is language-independent, requiring no hand-crafted language-specific features, and includes pre-trained models and a simple fit/score/analyze API.

Use cases

• extract named entities from text
• train a custom NER model in Python
• part-of-speech tagging with deep learning
• sequence labeling for any language
• semantic role labeling
• use ELMo embeddings for NER

When to choose

• you need a Keras-based NER or POS tagging model trainable on your own data
• you want language-independent sequence labeling without feature engineering
• you want a simple high-level API with pre-trained models

When to avoid

• you need actively maintained state-of-the-art transformer-based NER (e.g. spaCy, Hugging Face)
• you don't work in the Keras/TensorFlow ecosystem
• you need production support or recent updates

Facets

library · maturity maintenance

nlp machine-learning deep-learning machine-learning deep-learning python named-entity-recognition pos-tagging sequence-labeling keras elmo lstm-crf natural-language-processing

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markdown · JSON · MCP: product_card(name="Hironsan/anago")