Update README.md

README.md

@@ -30,15 +30,34 @@ pip install -U sentence-transformers
 Then you can use the model like this:
 
 ```python
-from sentence_transformers import SentenceTransformer
-sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."]
+from sentence_transformers import SentenceTransformer, util
 
-model = SentenceTransformer('nickprock/sentence-bert-base-italian-uncased')
-embeddings = model.encode(sentences)
-print(embeddings)
+query = "Quante persone vivono a Londra?"
+docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]
 
+#Load the model
+model = SentenceTransformer('nickprock/mmarco-bert-base-italian-uncased')
+
+#Encode query and documents
+query_emb = model.encode(query)
+doc_emb = model.encode(docs)
+
+#Compute dot score between query and all document embeddings
+scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
+
+#Combine docs & scores
+doc_score_pairs = list(zip(docs, scores))
+
+#Sort by decreasing score
+doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+
+#Output passages & scores
+for doc, score in doc_score_pairs:
+    print(score, doc)
 ```
 
+
+
 ## Usage (HuggingFace Transformers)
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
@@ -55,7 +74,8 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['Una ragazza si acconcia i capelli.', 'Una ragazza si sta spazzolando i capelli.']
+query = "Quante persone vivono a Londra?"
+docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('nickprock/sentence-bert-base-italian-uncased')
@@ -73,7 +93,6 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 
 print("Sentence embeddings:")
 print(sentence_embeddings)
-
 ```