Kashgari
Overview | Performance | Installation | Documentation | Contributing
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
SavedModelformat 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:
- Tutorial 1: Text Classification
- Tutorial 2: Text Labeling
- Tutorial 3: Seq2Seq
- Tutorial 4: Language Embedding
There are also articles and posts that illustrate how to use Kashgari:
- 基于 Kashgari 2 的短文本分类: 数据分析和预处理
- 基于 Kashgari 2 的短文本分类: 训练模型和调优
- 基于 Kashgari 2 的短文本分类: 模型部署
- 15 分钟搭建中文文本分类模型
- 基于 BERT 的中文命名实体识别（NER)
- BERT/ERNIE 文本分类和部署
- 五分钟搭建一个基于BERT的NER模型
- Multi-Class Text Classification with Kashgari in 15 minutes
Examples:
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.