Kashgari

Overview | Performance | Quick start | Documentation | Contributing

🎉🎉🎉 We are proud to announce that we entirely rewrote Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! 🎉🎉🎉

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.

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.

Eliyar Eziz 📖 ⚠️ 💻

Alex Wang 💻

Yusup 💻

Adline 💻

Road Map

• Based on TensorFlow 2.0+ [@BrikerMan]
• Fully support generator based training (#336 ,#273) [@BrikerMan]
• Clean code and full document
• Multi GPU/TPU Support [@BrikerMan]
• Embeddings
  • Bare Embedding [@BrikerMan]
  • Word Embedding (Load trained W2V) [@BrikerMan]
  • BERT Embedding (Based on bert4keras, support BERT, RoBERTa, ALBERT...) (#316) [@BrikerMan]
  • GPT-2 Embedding
  • FeaturesEmbedding (Support Numeric feature as input)
  • Stacked Embedding (Stack Text embedding and features Embedding)
• Classification Task
• Labeling Task
• Seq2Seq Task
• Built-in Callbacks
  • Evaluate Callback
  • Save Best Callback
• Support TensorFlow Hub (Optional)