Update README.md

README.md

@@ -9,22 +9,36 @@ tags:
 ---
 # shibing624/text2vec
 This is a CoSENT(Cosine Sentence) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
 ## Usage (text2vec)
 Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
+
 ```
 pip install -U text2vec
 ```
+
 Then you can use the model like this:
+
 ```python
-from text2vec import SBert
+from text2vec import SentenceModel
 sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
 
-model = SBert('shibing624/text2vec-base-chinese')
+model = SentenceModel('shibing624/text2vec-base-chinese')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
+
 ## Usage (HuggingFace Transformers)
-Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: 
+
+First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+Install transformers:
+```
+pip install transformers
+```
+
+Then load model and predict:
 ```python
 from transformers import BertTokenizer, BertModel
 import torch
@@ -50,6 +64,28 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
+
+## Usage (sentence-transformers)
+[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
+
+Install sentence-transformers:
+```
+pip install -U sentence-transformers
+```
+
+Then load model and predict:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+m = SentenceTransformer("shibing624/text2vec-base-chinese")
+sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
+
+sentence_embeddings = m.encode(sentences)
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
 ## Evaluation Results
 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [text2vec](https://github.com/shibing624/text2vec)