Update README.md

README.md

@@ -1122,7 +1122,7 @@ model-index:
     - type: precision_at_3
       value: 42.667
     - type: precision_at_5
-      value: 36.0
+      value: 36
     - type: recall_at_1
       value: 6.669
     - type: recall_at_10
@@ -1777,7 +1777,7 @@ model-index:
     - type: map_at_5
       value: 9.92
     - type: mrr_at_1
-      value: 23.0
+      value: 23
     - type: mrr_at_10
       value: 33.78
     - type: mrr_at_100
@@ -1789,7 +1789,7 @@ model-index:
     - type: mrr_at_5
       value: 32.565
     - type: ndcg_at_1
-      value: 23.0
+      value: 23
     - type: ndcg_at_10
       value: 19.863
     - type: ndcg_at_100
@@ -1801,7 +1801,7 @@ model-index:
     - type: ndcg_at_5
       value: 16.384
     - type: precision_at_1
-      value: 23.0
+      value: 23
     - type: precision_at_10
       value: 10.39
     - type: precision_at_100
@@ -2224,7 +2224,7 @@ model-index:
     - type: map_at_5
       value: 0.9039999999999999
     - type: mrr_at_1
-      value: 68.0
+      value: 68
     - type: mrr_at_10
       value: 81.01899999999999
     - type: mrr_at_100
@@ -2236,7 +2236,7 @@ model-index:
     - type: mrr_at_5
       value: 80.733
     - type: ndcg_at_1
-      value: 63.0
+      value: 63
     - type: ndcg_at_10
       value: 65.913
     - type: ndcg_at_100
@@ -2248,7 +2248,7 @@ model-index:
     - type: ndcg_at_5
       value: 66.69699999999999
     - type: precision_at_1
-      value: 68.0
+      value: 68
     - type: precision_at_10
       value: 71.6
     - type: precision_at_100
@@ -2258,7 +2258,7 @@ model-index:
     - type: precision_at_3
       value: 72.667
     - type: precision_at_5
-      value: 74.0
+      value: 74
     - type: recall_at_1
       value: 0.189
     - type: recall_at_10
@@ -2492,13 +2492,11 @@ model-index:
     task:
       type: PairClassification
 tags:
-- sentence-transformers
 - feature-extraction
 - sentence-similarity
 - mteb
 - onnx
 - teradata
-
 ---
 # A Teradata Vantage compatible Embeddings Model
 
@@ -2652,5 +2650,4 @@ print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(em
 print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
 ```
 
-You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)
-
+You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)