Create README.md

README.md

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+---
+language: zh
+tags:
+- sbert
+datasets:
+- dialogue
+---
+
+# Data
+train data is similarity sentence data from E-commerce dialogue, about 50w sentence pairs.
+
+## Model
+model created by [sentence-tansformers](https://www.sbert.net/index.html)，model struct is bi-encoder
+
+### Usage
+```python
+>>> from sentence_transformers import SentenceTransformer, util
+>>> model = SentenceTransformer("tuhailong/bi_encoder_roberta-wwm-ext", device="cuda:1") 
+>>> model.max_seq_length=32
+>>> sentences = ["今天天气不错", "今天心情不错"]
+>>> embeddings1 = model.encode([sentences[0]], convert_to_tensor=True)
+>>> embeddings2 = model.encode([sentences[1]], convert_to_tensor=True)
+>>> scores = util.cos_sim(embeddings1, embeddings2).cpu().numpy()
+>>> print(scores)
+```
+
+### PS
+Because add the pooling layer and dense layer after model，has folders in model files. So here will
+be additional files "1_Pooling-config.json", "2_Dense-config.json" and "2_Dense-pytorch_model.bin".
+after download these files, rename them as "1_Pooling/config.json", "2_Dense/config.json" and "2_Dense/pytorch_model.bin".