📝 Writing docs.

README.md

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 ## Overview
 
-Kashgare is simple and powerful NLP framework, build your state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks.
+Kashgari is 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**. Kashgare's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
-- **Powerful and simple**. Kashgare 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.
-- **Keras based**. Kashgare builds directly on Keras, making it easy to train your models and experiment with new approaches using different embeddings and model structure.
-- **Buildin transfer learning**. Kashgare build-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
-- **Fully scalable**. Kashgare provide a simple, fast, and scalable environment for fast experimentation.
+- **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.
+- **tf.Keras based**. Kashgari builds directly on tf.Keras, making it easy to train your models and experiment with new approaches using different embeddings and model structure.
+- **Buildin transfer learning**. Kashgari build-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
+- **Fully scalable**. Kashgari provide a simple, fast, and scalable environment for fast experimentation.
 
 ## Performance