Kashgari

Open source

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

2.4K stars0 likes2 views

Website GitHub

About

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

Kashgari

</a>
<a href="https://join.slack.com/t/kashgari/shared_invite/enQtODU4OTEzNDExNjUyLTY0MzI4MGFkZmRkY2VmMzdmZjRkZTYxMmMwNjMyOTI1NGE5YzQ2OTZkYzA1YWY0NTkyMDdlZGY5MGI5N2U4YzM">
    
</a>
<a href="https://travis-ci.com/BrikerMan/Kashgari">
    
</a>
<a href='https://coveralls.io/github/BrikerMan/Kashgari?branch=master'>
    
</a>
 <a href="https://pepy.tech/project/kashgari">
    
</a>
<a href="https://pypi.org/project/kashgari/">
    
</a>

Overview | Performance | Installation | Documentation | Contributing

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
TensorFlow 2.2+	`pip install 'kashgari>=2.0.2'`	TF2.10+ with tf.keras
TensorFlow 1.14+	`pip install 'kashgari>=1.0.0,<2.0.0'`	TF1.14+ with tf.keras
Keras	`pip install 'kashgari<1.0.0'`	keras version

You also need to install tensorflow_addons with TensorFlow.

TensorFlow Version	tensorflow_addons version
TensorFlow 2.1	`pip install tensorflow_addons==0.9.1`
TensorFlow 2.2	`pip install tensorflow_addons==0.11.2`
TensorFlow 2.3, 2.4, 2.5	`pip install tensorflow_addons==0.13.0`

Tutorials

Here is a set of quick tutorials to get you started with the library:

There are also articles and posts that illustrate how to use Kashgari:

Examples:

Neural machine translation with Seq2Seq

Contributors ✨

Thanks goes to these wonderful people. And there are many ways to get involved. Start with the contributor guidelines and then check these open issues for specific tasks.