Update README.md

README.md

@@ -75,7 +75,6 @@ model-index:
 # bge-large-en-v1.5
 
 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
 ## Model Details
 
 ### Model Description
@@ -120,7 +119,8 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("DannyAI/embedding_fine_tuning_adaptive_layer_matryoshka2dloss_bge_large_en_v1.5")
+matryoshka_dims = [768,512,256,128,64] # for truncateing the dimensions
+model = SentenceTransformer("DannyAI/embedding_fine_tuning_adaptive_layer_matryoshka2dloss_bge_large_en_v1.5", truncate_dim=matryoshka_dims[0])
 # Run inference
 sentences = [
     'Young boy kicks a soccer ball towards the goal as the crowd watches.',
@@ -129,7 +129,7 @@ sentences = [
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
-# [3, 1024]
+# [3, 768]
 
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)