Update README.md

README.md

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 This is the sentence embedding model pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658).
 
+## How to use
+
+You can use this model to extract sentence embeddings for sentence similarity task. We use cosine distance to calculate the embedding similarity here: 
+
+```python
+>>> from sentence_transformers import SentenceTransformer
+>>> model = SentenceTransformer('uer/sbert-base-chinese-nli')
+>>> sentences = ['那个人很开心', '那个人非常开心']
+>>> sentence_embeddings = model.encode(sentences)
+>>> from sklearn.metrics.pairwise import paired_cosine_distances
+>>> cosine_score = 1 - paired_cosine_distances([sentence_embeddings[0]],[sentence_embeddings[1]])
+```
+
 ## Training data
 
 [ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/) is used as training data.