Add Sentence Transformers snippet to README (#2)

- Add Sentence Transformers snippet to README (03ce83e345bce72437e8ffa8935594a14a93fad9)

README.md

@@ -3223,7 +3223,7 @@ Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
 
 ### Why mean pooling?
 
-`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
+`mean pooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
 It has been proved to be the most effective way to produce high-quality sentence embeddings.
 We offer an `encode` function to deal with this.
 
@@ -3256,7 +3256,7 @@ embeddings = F.normalize(embeddings, p=2, dim=1)
 </p>
 </details>
 
-You can use Jina Embedding models directly from transformers package:
+You can use Jina Embedding models directly from the `transformers` package:
 ```python
 !pip install transformers
 from transformers import AutoModel
@@ -3277,7 +3277,22 @@ embeddings = model.encode(
 )
 ```
 
-## Alternatives to Using Transformers Package
+Or you can use the model with the `sentence-transformers` package:
+```python
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer("jinaai/jina-embeddings-v2-base-es", trust_remote_code=True)
+embeddings = model.encode(['How is the weather today?', '¿Qué tiempo hace hoy?'])
+print(util.cos_sim(embeddings[0], embeddings[1]))
+```
+
+And if you only want to handle shorter sequence, such as 2k, then you can set the `model.max_seq_length`
+
+```python
+model.max_seq_length = 2048
+```
+
+## Alternatives to Transformers and Sentence Transformers
 
 1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 
 2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).