🎉🎉🎉 We released the 2.0.0 version with TF2 Support. 🎉🎉🎉
If you use this project for your research, please cite:
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.
- 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.
Welcome to add performance report.
|Named Entity Recognition||Chinese||People’s Daily Ner Corpus||95.57|
The project is based on Python 3.6+, because it is 2019 and type hinting is cool.
||TF2.10+ with tf.keras|
||TF1.14+ with tf.keras|
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:
- 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
- Multi-Class Text Classification with Kashgari in 15 minutes