BrikerMan/Kashgari

Health v2 · maintenance only

Adoption not part of the score

2381 stars · 431 forks observed · 2026-08-28

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

Kashgari is a Keras/TensorFlow 2-based NLP transfer learning framework for building text labeling (NER, PoS) and text classification models quickly. It bundles pre-trained BERT, GPT-2, and Word2Vec embeddings and supports exporting models in SavedModel format for production deployment.

Use cases

• train a named entity recognition model with BERT embeddings
• build a text classifier in a few lines of Keras code
• do part-of-speech tagging with pre-trained language models
• fine-tune BERT or GPT-2 embeddings for sequence labeling
• export an NLP model as SavedModel for production serving
• experiment with different embeddings and model architectures for NLP tasks

When to choose

• you want a simple, human-friendly Keras API for NER, PoS, or text classification
• you need built-in BERT/GPT-2/Word2Vec transfer learning without wiring transformers yourself
• you want to export trained models to TensorFlow SavedModel for production

When to avoid

• you need PyTorch or the latest Hugging Face transformers ecosystem
• you require cutting-edge model architectures beyond sequence labeling and classification
• you need a project with frequent recent development activity

Facets

framework · maturity maintenance

nlp machine-learning deep-learning machine-learning deep-learning python transfer-learning named-entity-recognition text-classification sequence-labeling bert gpt-2 word2vec keras tensorflow natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="BrikerMan/Kashgari")