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Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
| Date | Stars |
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| 2026-07-24 | 2382 |
| 2026-07-25 | 2382 |
| 2026-07-28 | 2382 |
| 2026-07-30 | 2382 |
| 2026-08-06 | 2382 |
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<h1 align="center">
<a href='https://en.wikipedia.org/wiki/Mahmud_al-Kashgari'>Kashgari</a>
</h1>
<p align="center">
<a href="https://github.com/BrikerMan/kashgari/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/BrikerMan/kashgari.svg?color=blue&style=popout">
</a>
<a href="https://join.slack.com/t/kashgari/shared_invite/enQtODU4OTEzNDExNjUyLTY0MzI4MGFkZmRkY2VmMzdmZjRkZTYxMmMwNjMyOTI1NGE5YzQ2OTZkYzA1YWY0NTkyMDdlZGY5MGI5N2U4YzM">
<img alt="Slack" src="https://img.shields.io/badge/chat-Slack-blueviolet?logo=Slack&style=popout">
</a>
<a href="https://travis-ci.com/BrikerMan/Kashgari">
<img src="https://travis-ci.com/BrikerMan/Kashgari.svg?branch=master"/>
</a>
<a href='https://coveralls.io/github/BrikerMan/Kashgari?branch=master'>
<img src='https://coveralls.io/repos/github/BrikerMan/Kashgari/badge.svg?branch=master' alt='Coverage Status'/>
</a>
<a href="https://pepy.tech/project/kashgari">
<img src="https://pepy.tech/badge/kashgari"/>
</a>
<a href="https://pypi.org/project/kashgari/">
<img alt="PyPI" src="https://img.shields.io/pypi/v/kashgari.svg">
</a>
</p>
<h4 align="center">
<a href="#overview">Overview</a> |
<a href="#performance">Performance</a> |
<a href="#installation">Installation</a> |
<a href="https://kashgari.readthedocs.io/">Documentation</a> |
<a href="https://kashgari.readthedocs.io/about/contributing/">Contributing</a>
</h4>
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🎉🎉🎉 We released the 2.0.0 version with TF2 Support. 🎉🎉🎉
If you use this project for your research, please cite:
```
@misc{Kashgari
author = {Eliyar Eziz},
title = {Kashgari},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/BrikerMan/Kashgari}}
}
```
## Overview
Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.
- **Human-friendly**. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
- **Powerful and simple**. Kashgari allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification.
- **Built-in transfer learning**. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
- **Fully scalable**. Kashgari provides a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure.
- **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on the cloud.
## Our Goal
- **Academic users** Easier experimentation to prove their hypothesis without coding from scratch.
- **NLP beginners** Learn how to build an NLP project with production level code quality.
- **NLP developers** Build a production level classification/labeling model within minutes.
## Performance
Welcome to add performance report.
| Task | Language | Dataset | Score |
| -------------------------- | -------- | --------------------------- | ----- |
| [Named Entity Recognition] | Chinese | [People's Daily Ner Corpus] | 95.57 |
| [Text Classification] | Chinese | [SMP2018ECDTCorpus] | 94.57 |
## Installation
The project is based on Python 3.6+, because it is 2019 and type hinting is cool.
| Backend | kashgari version | desc |
| ---------------- | -------------------------------------- | --------------------- |
|Excerpt of 6,559 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:980e9606741db58e, topic:nlp, topic:named-entity-recognition, topic:text-classification
