Fix the distance of vector labels

README.md

@@ -7,7 +7,7 @@ tags:
 
 ---
 
-# vector-ordinal
+# vector-euclidean
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -27,7 +27,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('vector-ordinal')
+model = SentenceTransformer('vector-euclidean')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -38,7 +38,7 @@ print(embeddings)
 
 <!--- 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=vector-ordinal)
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=vector-euclidean)
 
 
 ## Training

eval/similarity_evaluation_vector-euclidean_results.csv

@@ -0,0 +1,4 @@
+epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
+0,-1,-0.8308199944900635,-0.35600124718369053,-0.5885619213060179,-0.3560012467988433,-0.5839677989747931,-0.35600124679848966,-0.830819997976803,-0.3560012472714895
+1,-1,-0.8256075524194896,-0.3560012476390819,-0.5918772590836299,-0.3560012468011841,-0.5884111865115857,-0.3560012468005801,-0.825607551967303,-0.35600124783668935
+2,-1,-0.8268738805822666,-0.3560012479969469,-0.5908985895093282,-0.3560012468025139,-0.5876269516036872,-0.35600124680206685,-0.826873880364517,-0.3560012483265016

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:749c3e7b123638aca4ecac9a5b00e46c6129fe6ac908e3b2fde36b9fd4d35268
+oid sha256:17671a2adb091b8aa695d2619fb068002d1a51b779972e78fcac19bd2cbf2916
 size 437967672