Add usage examples to README.md

README.md

@@ -29,6 +29,36 @@ It achieves the following results on the evaluation set:
 - F1 Score: 0.9322
 
 
+## Usage
+
+Pre-trained models can be used like this:
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('Jsevisal/CrossEncoder-ModernBERT-base-qnli')
+scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')])
+
+#e.g.
+scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')])
+```
+
+## Usage with Transformers AutoModel
+You can use the model also directly with Transformers library (without SentenceTransformers library):
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained('Jsevisal/CrossEncoder-ModernBERT-base-qnli')
+tokenizer = AutoTokenizer.from_pretrained('Jsevisal/CrossEncoder-ModernBERT-base-qnli')
+
+features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+    scores = torch.nn.functional.sigmoid(model(**features).logits)
+    print(scores)
+```
+
+
 ## Training procedure
 
 ### Training hyperparameters