🎉🎉🎉 We released the 2.0.0-alpha2 version with Seq2Seq Support. 🎉🎉🎉
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
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.
- Text Classification Model
- Text Labeling Model
- Seq2Seq Model
- Classification Models
- Labeling Models
- Data Processors