Kashgari
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
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.
1 maintainer
Release files for kashgari 2.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| kashgari-2.0.2.tar.gz | 53.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| kashgari-2.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size:143.0 kB
Release files / kashgari-2.0.2.tar.gz
| Download URL | kashgari-2.0.2.tar.gz |
|---|---|
| Size | 53.8 kB |
| Tags | Source |
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Release files / kashgari-2.0.2-py3-none-any.whl
| Download URL | kashgari-2.0.2-py3-none-any.whl |
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| Size | 89.3 kB |
| Tags | Python 3 |
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