Update README.md

README.md

@@ -1,7 +1,6 @@
 ---
 language:
 - en
-library_name: transformers
 license: apache-2.0
 model-index:
 - name: gte-base-en-v1.5
@@ -377,7 +376,7 @@ model-index:
     - type: mrr_at_3
       value: 51.285000000000004
     - type: mrr_at_5
-      value: 53.0
+      value: 53
     - type: ndcg_at_1
       value: 42.571
     - type: ndcg_at_10
@@ -387,7 +386,7 @@ model-index:
     - type: ndcg_at_1000
       value: 62.426
     - type: ndcg_at_3
-      value: 51.0
+      value: 51
     - type: ndcg_at_5
       value: 54.346000000000004
     - type: precision_at_1
@@ -2197,7 +2196,7 @@ model-index:
     - type: recall_at_100
       value: 96.5
     - type: recall_at_1000
-      value: 100.0
+      value: 100
     - type: recall_at_3
       value: 77.86699999999999
     - type: recall_at_5
@@ -2331,7 +2330,7 @@ model-index:
     - type: map_at_5
       value: 0.918
     - type: mrr_at_1
-      value: 84.0
+      value: 84
     - type: mrr_at_10
       value: 91.067
     - type: mrr_at_100
@@ -2343,7 +2342,7 @@ model-index:
     - type: mrr_at_5
       value: 91.067
     - type: ndcg_at_1
-      value: 78.0
+      value: 78
     - type: ndcg_at_10
       value: 73.13499999999999
     - type: ndcg_at_100
@@ -2355,7 +2354,7 @@ model-index:
     - type: ndcg_at_5
       value: 72.74199999999999
     - type: precision_at_1
-      value: 84.0
+      value: 84
     - type: precision_at_10
       value: 78.8
     - type: precision_at_100
@@ -2365,7 +2364,7 @@ model-index:
     - type: precision_at_3
       value: 77.333
     - type: precision_at_5
-      value: 78.0
+      value: 78
     - type: recall_at_1
       value: 0.22300000000000003
     - type: recall_at_10
@@ -2599,14 +2598,11 @@ model-index:
     task:
       type: PairClassification
 tags:
-- sentence-transformers
 - gte
 - mteb
-- transformers.js
 - sentence-similarity
 - onnx
 - teradata
-
 ---
 # A Teradata Vantage compatible Embeddings Model
 
@@ -2758,5 +2754,4 @@ 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)