add sentence transformer usage

README.md

@@ -2622,7 +2622,7 @@ Follow us on:
 
 
 # Usage
-
+## 1. angle_emb
 
 ```bash
 python -m pip install -U angle-emb
@@ -2630,31 +2630,66 @@ python -m pip install -U angle-emb
 
 1) Non-Retrieval Tasks
 
+There is no need to specify any prompts.
+
 ```python
 from angle_emb import AnglE
+from scipy import spatial
 
 angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
-vec = angle.encode('hello world', to_numpy=True)
-print(vec)
-vecs = angle.encode(['hello world1', 'hello world2'], to_numpy=True)
-print(vecs)
+doc_vecs = angle.encode([
+    'The weather is great!',
+    'The weather is very good!',
+    'i am going to bed'
+])
+
+for i, dv1 in enumerate(doc_vecs):
+    for dv2 in doc_vecs[i+1:]:
+        print(1 - spatial.distance.cosine(dv1, dv2))
 ```
 
 2) Retrieval Tasks
 
-For retrieval purposes, please use the prompt `Prompts.C`.
+For retrieval purposes, please use the prompt `Prompts.C` for query (not for document).
 
 ```python
 from angle_emb import AnglE, Prompts
+from scipy import spatial
 
 angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
-angle.set_prompt(prompt=Prompts.C)
-vec = angle.encode({'text': 'hello world'}, to_numpy=True)
-print(vec)
-vecs = angle.encode([{'text': 'hello world1'}, {'text': 'hello world2'}], to_numpy=True)
-print(vecs)
+qv = angle.encode(Prompts.C.format(text='what is the weather?'))
+doc_vecs = angle.encode([
+    'The weather is great!',
+    'it is rainy today.',
+    'i am going to bed'
+])
+
+for dv in doc_vecs:
+    print(1 - spatial.distance.cosine(qv[0], dv))
+```
+
+## 2. sentence transformer
+
+
+```python
+from angle_emb import Prompts
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("WhereIsAI/UAE-Large-V1").cuda()
+
+qv = model.encode(Prompts.C.format(text='what is the weather?'))
+doc_vecs = model.encode([
+    'The weather is great!',
+    'it is rainy today.',
+    'i am going to bed'
+])
+
+for dv in doc_vecs:
+    print(1 - spatial.distance.cosine(qv, dv))
 ```
 
+
+
 # Citation 
 
 If you use our pre-trained models, welcome to support us by citing our work: