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https://github.com/BrikerMan/Kashgari

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.
https://github.com/BrikerMan/Kashgari

bert bert-model gpt-2 machine-learning named-entity-recognition ner nlp nlp-framework seq2seq sequence-labeling text-classification text-labeling transfer-learning

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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.

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    Kashgari






    

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    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:



- [Tutorial 1: Text Classification](./docs/tutorial/text-classification.md)

- [Tutorial 2: Text Labeling](./docs/tutorial/text-labeling.md)

- [Tutorial 3: Seq2Seq](./docs/tutorial/seq2seq.md)

- [Tutorial 4: Language Embedding](./docs/embeddings/index.md)



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](https://eliyar.biz/short_text_classificaion_with_kashgari_v2_part_1/index.html)

- [基于 Kashgari 2 的短文本分类: 训练模型和调优](https://eliyar.biz/nlp/short_text_classificaion_with_kashgari_v2_part_2/index.html)

- [基于 Kashgari 2 的短文本分类: 模型部署](https://eliyar.biz/nlp/short_text_classificaion_with_kashgari_v2_part_3/index.html)

- [15 分钟搭建中文文本分类模型](https://eliyar.biz/nlp_chinese_text_classification_in_15mins/)

- [基于 BERT 的中文命名实体识别（NER)](https://eliyar.biz/nlp_chinese_bert_ner/)

- [BERT/ERNIE 文本分类和部署](https://eliyar.biz/nlp_train_and_deploy_bert_text_classification/)

- [五分钟搭建一个基于BERT的NER模型](https://www.jianshu.com/p/1d6689851622)

- [Multi-Class Text Classification with Kashgari in 15 minutes](https://medium.com/@BrikerMan/multi-class-text-classification-with-kashgari-in-15mins-c3e744ce971d)



Examples:



- [Neural machine translation with Seq2Seq](./examples/translate_with_seq2seq.ipynb)



## Contributors ✨



Thanks goes to these wonderful people. And there are many ways to get involved.

Start with the [contributor guidelines](./docs/about/contributing.md) and then check these open issues for specific tasks.



[Named Entity Recognition]: /tutorial/text-labeling/#chinese-ner-performance

[People's Daily Ner Corpus]: /apis/corpus/#kashgari.corpus.ChineseDailyNerCorpus

[Text Classification]: /tutorial/text-classification/#short-sentence-classification-performance

[SMP2018ECDTCorpus]: /apis/corpus/#kashgari.corpus.SMP2018ECDTCorpus