upload

README.md

@@ -7,7 +7,7 @@ tags:
 - transformers
 ---
 
-# msmarco-bert-base-dot-v4
+# msmarco-distilbert-base-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-bert-base-dot-v4')
+model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-dot-v4')
 
 #Encode query and documents
 query_emb = model.encode(query)
@@ -81,8 +81,8 @@ 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-bert-base-dot-v4")
-model = AutoModel.from_pretrained("sentence-transformers/msmarco-bert-base-dot-v4")
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-dot-v4")
+model = AutoModel.from_pretrained("sentence-transformers/msmarco-distilbert-base-dot-v4")
 
 #Encode query and docs
 query_emb = encode(query)
@@ -119,7 +119,7 @@ In the following some technical details how this model must be used:
 
 <!--- Describe how your model was evaluated -->
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=msmarco-bert-base-dot-v4)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=msmarco-distilbert-base-dot-v4)
 
 
 ## Training