update

README.md

@@ -6,8 +6,8 @@ tags:
 - sentence-similarity
 ---
 
-# multi-qa-MiniLM-L6-cos-v1
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
+# multi-qa-distilbert-cos-v1
+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 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html)
 
 
 ## Usage (Sentence-Transformers)
@@ -25,7 +25,7 @@ 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/multi-qa-MiniLM-L6-cos-v1')
+model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1')
 
 #Encode query and documents
 query_emb = model.encode(query)
@@ -84,8 +84,8 @@ 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/multi-qa-MiniLM-L6-cos-v1")
-model = AutoModel.from_pretrained("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
+tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-distilbert-cos-v1")
+model = AutoModel.from_pretrained("sentence-transformers/multi-qa-distilbert-cos-v1")
 
 #Encode query and docs
 query_emb = encode(query)
@@ -111,7 +111,7 @@ In the following some technical details how this model must be used:
 
 | Setting | Value |
 | --- | :---: |
-| Dimensions | 384 |
+| Dimensions | 768 |
 | Produces normalized embeddings | Yes |
 | Pooling-Method | Mean pooling |
 | Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |
@@ -145,7 +145,7 @@ The full training script is accessible in this current repository: `train_script
 
 ### Pre-training 
 
-We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
+We use the pretrained [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
 
 #### Training