Update README.md

README.md

@@ -22,11 +22,14 @@ Fine tunes a cross encoder on the Amazon ESCI dataset.
 
 # Usage
 
+## Transformers
+
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from torch import no_grad
 
 model_name = "lv12/esci-ms-marco-MiniLM-L-12-v2"
 
@@ -56,7 +59,38 @@ inputs = tokenizer(
     truncation=True,
     return_tensors="pt",
 )
-scores = model(**inputs).logits.cpu().detach().numpy()
+
+model.eval()
+with no_grad():
+    scores = model(**inputs).logits.cpu().detach().numpy()
+    print(scores)
+```
+
+### Sentence Transformers
+
+```python
+from sentence_transformers import CrossEncoder
+
+model_name = "lv12/esci-ms-marco-MiniLM-L-12-v2"
+
+queries = [
+    "adidas shoes",
+    "adidas sambas",
+    "girls sandals",
+    "backpacks",
+    "shoes", 
+    "mustard blouse"
+]
+documents =  [
+        "Nike Air Max, with air cushion",
+        "Adidas Ultraboost, the best boost you can get",
+        "Women's sandals wide width 9",
+        "Girl's surf backpack",
+        "Fresh watermelon, all you can eat",
+        "Floral yellow dress with frills and lace"
+    ]
+model = CrossEncoder(model_name, max_length=512)
+scores = model.predict([(q, d) for q, d in zip(queries, documents)])
 print(scores)
 ```