Update README outputs + dim (768 -> 1024)

README.md

@@ -2950,12 +2950,12 @@ doc_embeddings = model.encode([
     "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
 ])
 print(query_embeddings.shape, doc_embeddings.shape)
-# (2, 768) (1, 768)
+# (2, 1024) (1, 1024)
 
 similarities = model.similarity(query_embeddings, doc_embeddings)
 print(similarities)
-# tensor([[0.7214],
-#         [0.3260]])
+# tensor([[0.6518],
+#         [0.4237]])
 ```
 
 <details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary>
@@ -2979,8 +2979,8 @@ print(query_embeddings.shape, doc_embeddings.shape)
 
 similarities = model.similarity(query_embeddings, doc_embeddings)
 print(similarities)
-# tensor([[0.7759],
-#         [0.3419]])
+# tensor([[0.6835],
+#         [0.3982]])
 ```
 
 Note the small differences compared to the full 1024-dimensional similarities.
@@ -3023,12 +3023,12 @@ query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
 doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
 doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
 print(query_embeddings.shape, doc_embeddings.shape)
-# torch.Size([2, 768]) torch.Size([1, 768])
+# torch.Size([2, 1024]) torch.Size([1, 1024])
 
 similarities = query_embeddings @ doc_embeddings.T
 print(similarities)
-# tensor([[0.7214],
-#         [0.3260]])
+# tensor([[0.6518],
+#         [0.4237]])
 ```
 
 <details><summary>Click to see Transformers usage with Matryoshka Truncation</summary>
@@ -3076,11 +3076,11 @@ print(query_embeddings.shape, doc_embeddings.shape)
 
 similarities = query_embeddings @ doc_embeddings.T
 print(similarities)
-# tensor([[0.7759],
-#         [0.3419]])
+# tensor([[0.6835],
+#         [0.3982]])
 ```
 
-Note the small differences compared to the full 768-dimensional similarities.
+Note the small differences compared to the full 1024-dimensional similarities.
 
 </details>
 
@@ -3116,7 +3116,7 @@ const doc_embeddings = await extractor([
 
 // Compute similarity scores
 const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
-console.log(similarities.tolist()); // [[0.721383273601532], [0.3259955644607544]]
+console.log(similarities.tolist());
 ```