Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 ---
 
 # msmarco-MiniLM-L6-cos-v5
-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 Passages 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)
+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 500k (query, answer) pairs from the [MS MARCO Passages 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)
 
 
 ## Usage (Sentence-Transformers)
@@ -112,7 +112,7 @@ In the following some technical details how this model must be used:
 
 | Setting | Value |
 | --- | :---: |
-| Dimensions | 768 |
+| Dimensions | 384 |
 | Produces normalized embeddings | Yes |
 | Pooling-Method | Mean pooling |
 | Suitable score functions | dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance |