Update README.md

README.md

@@ -9,7 +9,7 @@ tags:
 
 # embaas/sentence-transformers-e5-large-v2
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a the sentence-transformers version of the [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
 <!--- Describe your model here -->
 
@@ -32,13 +32,35 @@ embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
+## Using with API
+
+You can use the [embaas API](https://embaas.io) to encode your input. Get your free API key from [embaas.io](https://embaas.io)
+
+```python
+import requests
+ 
+url = "https://api.embaas.io/v1/embeddings/"
+ 
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": "Bearer ${YOUR_API_KEY}"
+}
+ 
+data = {
+    "texts": ["This is an example sentence.", "Here is another sentence."],
+    "instruction": "query"
+    "model": "e5-large-v2"
+}
+ 
+response = requests.post(url, json=data, headers=headers)
+```
 
 
 ## Evaluation Results
 
 <!--- Describe how your model was evaluated -->
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=embaas/sentence-transformers-e5-large-v2)
+Find the results of the e5 at the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard)