Metadata-Version: 2.1
Name: kashgari
Version: 2.0.0a2
Summary: Simple, Keras-powered multilingual NLP framework, allows you to build your models in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks. Includes BERT, GPT-2 and word2vec embedding.
Home-page: https://github.com/BrikerMan/Kashgari
Author: BrikerMan
Author-email: eliyar917@gmail.com
License: Apache License 2.0
Description: <!-- prettier-ignore-start -->
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        <h1 align="center">
            <a href='https://en.wikipedia.org/wiki/Mahmud_al-Kashgari'>Kashgari</a>
        </h1>
        
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            <a href="https://github.com/BrikerMan/kashgari/blob/master/LICENSE">
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        <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-alpha2 version with Seq2Seq Support. 🎉🎉🎉
        
        ## 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.
        
        ## Supporting the project
        
        **You can support the project by checking out our sponsor page. It takes only one click:**
        
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        <a href="https://tracking.gitads.io/?repo=Kashgari">
          <img alt="Sponsor banner" src="https://images.gitads.io/Kashgari" />
        </a>
        <br>
        <i>
          This advert was placed by <a href="https://tracking.gitads.io/?repo=Kashgari">GitAds</a>
        </i>
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        ## 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   |
        | Neural machine translation |          |                             | // TODO |
        
        ## Installation
        
        The project is based on Python 3.6+, because it is 2019 and type hinting is cool.
        
        | Backend          | pypi version                           | desc                         |
        | ---------------- | -------------------------------------- | ---------------------------- |
        | TensorFlow 2.1+  | `pip install 'kashgari>=2.0.0a0'`      | TF2 tf.keras - alpha version |
        | TensorFlow 1.14+ | `pip install 'kashgari>=1.0.0,<2.0.0'` | TF1.14+ tf.keras version     |
        | Keras            | `pip install 'kashgari<1.0.0'`         | keras version                |
        
        ## 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:
        
        - [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
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >3.6
Description-Content-Type: text/markdown
