chore: update example code

README.md

@@ -38,7 +38,7 @@ As you can see, the `jina-reranker-v1-turbo-en` offers a balanced approach with
 
 # Usage
 
-The easiest way to starting using `jina-reranker-v1-turbo-en` is to use Jina AI's [Reranker API](https://jina.ai/reranker/).
+1. The easiest way to starting using `jina-reranker-v1-turbo-en` is to use Jina AI's [Reranker API](https://jina.ai/reranker/).
 
 ```bash
 curl https://api.jina.ai/v1/rerank \
@@ -63,14 +63,47 @@ curl https://api.jina.ai/v1/rerank \
 }'
 ```
 
-Alternatively, you can use the `transformers` library to interact with the model programmatically.
+2. Alternatively, you can use the latest version of the `sentence-transformers>=0.27.0` library. You can install it via pip:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then, you can use the following code to interact with the model:
+
+```python
+from sentence_transformers import CrossEncoder
+
+# Load the model, here we use our base sized model
+model = CrossEncoder("jina-reranker-v1-turbo-en", num_labels=1, automodel_args={'trust_remote_code': True})
+
+
+# Example query and documents
+query = "Organic skincare products for sensitive skin"
+documents = [
+    "Eco-friendly kitchenware for modern homes",
+    "Biodegradable cleaning supplies for eco-conscious consumers",
+    "Organic cotton baby clothes for sensitive skin",
+    "Natural organic skincare range for sensitive skin",
+    "Tech gadgets for smart homes: 2024 edition",
+    "Sustainable gardening tools and compost solutions",
+    "Sensitive skin-friendly facial cleansers and toners",
+    "Organic food wraps and storage solutions",
+    "All-natural pet food for dogs with allergies",
+    "Yoga mats made from recycled materials"
+]
+
+results = model.rank(query, documents, return_documents=True, top_k=3)
+```
+
+3. You can also use the `transformers` library to interact with the model programmatically.
 
 ```python
 !pip install transformers
 from transformers import AutoModelForSequenceClassification
 
 model = AutoModelForSequenceClassification.from_pretrained(
-    'jinaai/jina-reranker-v1-turbo-en', num_labels=1, trust_remote_code=True
+    'jina-reranker-v1-turbo-en', num_labels=1, trust_remote_code=True
 )
 
 # Example query and documents
@@ -94,6 +127,8 @@ sentence_pairs = [[query, doc] for doc in documents]
 scores = model.compute_score(sentence_pairs)
 ```
 
+That's it! You can now use the `jina-reranker-v1-turbo-en` model in your projects.
+
 # Evaluation
 
 We evaluated Jina Reranker on 3 key benchmarks to ensure top-tier performance and search relevance.