upload

README.md

@@ -7,7 +7,7 @@ tags:
 - transformers
 ---
 
-# msmarco-distilbert-base-dot-v4
+# msmarco-distilbert-dot-v4
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for **semantic search**. It has been trained on 500K (query, answer) pairs from the [MS MARCO dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking/). For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
 
 
@@ -26,7 +26,7 @@ query = "How many people live in London?"
 docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 
 #Load the model
-model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-dot-v4')
+model = SentenceTransformer('sentence-transformers/msmarco-distilbert-dot-v4')
 
 #Encode query and documents
 query_emb = model.encode(query)
@@ -42,6 +42,7 @@ doc_score_pairs = list(zip(docs, scores))
 doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
 
 #Output passages & scores
+print("Query:", query)
 for doc, score in doc_score_pairs:
     print(score, doc)
 ```
@@ -81,8 +82,8 @@ query = "How many people live in London?"
 docs = ["Around 9 Million people live in London", "London is known for its financial district"]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-dot-v4")
-model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-base-dot-v4")
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-dot-v4")
+model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-dot-v4")
 
 #Encode query and docs
 query_emb = encode(query)
@@ -98,6 +99,7 @@ doc_score_pairs = list(zip(docs, scores))
 doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
 
 #Output passages & scores
+print("Query:", query)
 for doc, score in doc_score_pairs:
     print(score, doc)
 ```