Add 'Sentence Transformers' Tag to generate sentence embedding

Hugging Face uses tags to formulate interface API, with 'Sentence Transformers' tag now the Interface API will generate a single embedding for the entire sentence.

README.md

@@ -1,6 +1,7 @@
 ---
 tags:
 - mteb
+- Sentence Transformers
 model-index:
 - name: multilingual-e5-large
   results:
@@ -577,7 +578,7 @@ model-index:
     - type: precision_at_1000
       value: 1.978
     - type: precision_at_3
-      value: 50.0
+      value: 50
     - type: precision_at_5
       value: 41.349999999999994
     - type: recall_at_1
@@ -3597,7 +3598,7 @@ model-index:
     - type: manhattan_precision
       value: 87.66564729867483
     - type: manhattan_recall
-      value: 86.0
+      value: 86
     - type: max_accuracy
       value: 99.74356435643564
     - type: max_ap
@@ -3678,7 +3679,7 @@ model-index:
     - type: map_at_5
       value: 0.885
     - type: mrr_at_1
-      value: 78.0
+      value: 78
     - type: mrr_at_10
       value: 86.56700000000001
     - type: mrr_at_100
@@ -3690,7 +3691,7 @@ model-index:
     - type: mrr_at_5
       value: 86.56700000000001
     - type: ndcg_at_1
-      value: 76.0
+      value: 76
     - type: ndcg_at_10
       value: 71.326
     - type: ndcg_at_100
@@ -3702,7 +3703,7 @@ model-index:
     - type: ndcg_at_5
       value: 73.833
     - type: precision_at_1
-      value: 78.0
+      value: 78
     - type: precision_at_10
       value: 74.8
     - type: precision_at_100
@@ -3710,9 +3711,9 @@ model-index:
     - type: precision_at_1000
       value: 21.836
     - type: precision_at_3
-      value: 78.0
+      value: 78
     - type: precision_at_5
-      value: 78.0
+      value: 78
     - type: recall_at_1
       value: 0.20400000000000001
     - type: recall_at_10
@@ -3837,13 +3838,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 96.0
+      value: 96
     - type: f1
       value: 94.86666666666666
     - type: precision
       value: 94.31666666666668
     - type: recall
-      value: 96.0
+      value: 96
   - task:
       type: BitextMining
     dataset:
@@ -4330,13 +4331,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 97.0
+      value: 97
     - type: f1
       value: 96.15
     - type: precision
       value: 95.76666666666668
     - type: recall
-      value: 97.0
+      value: 97
   - task:
       type: BitextMining
     dataset:
@@ -4449,13 +4450,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 95.0
+      value: 95
     - type: f1
       value: 93.60666666666667
     - type: precision
       value: 92.975
     - type: recall
-      value: 95.0
+      value: 95
   - task:
       type: BitextMining
     dataset:
@@ -4487,7 +4488,7 @@ model-index:
     - type: f1
       value: 94.52999999999999
     - type: precision
-      value: 94.0
+      value: 94
     - type: recall
       value: 95.7
   - task:
@@ -4791,7 +4792,7 @@ model-index:
     - type: accuracy
       value: 97.7
     - type: f1
-      value: 97.0
+      value: 97
     - type: precision
       value: 96.65
     - type: recall
@@ -5112,13 +5113,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 81.0
+      value: 81
     - type: f1
       value: 77.8232380952381
     - type: precision
       value: 76.60194444444444
     - type: recall
-      value: 81.0
+      value: 81
   - task:
       type: BitextMining
     dataset:
@@ -5129,13 +5130,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 91.0
+      value: 91
     - type: f1
       value: 88.70857142857142
     - type: precision
       value: 87.7
     - type: recall
-      value: 91.0
+      value: 91
   - task:
       type: BitextMining
     dataset:
@@ -5486,13 +5487,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 96.0
+      value: 96
     - type: f1
       value: 94.89
     - type: precision
       value: 94.39166666666667
     - type: recall
-      value: 96.0
+      value: 96
   - task:
       type: BitextMining
     dataset:
@@ -5537,13 +5538,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 88.0
+      value: 88
     - type: f1
       value: 85.47
     - type: precision
       value: 84.43266233766234
     - type: recall
-      value: 88.0
+      value: 88
   - task:
       type: BitextMining
     dataset:
@@ -5622,13 +5623,13 @@ model-index:
       revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
     metrics:
     - type: accuracy
-      value: 89.0
+      value: 89
     - type: f1
       value: 86.23190476190476
     - type: precision
       value: 85.035
     - type: recall
-      value: 89.0
+      value: 89
   - task:
       type: Retrieval
     dataset:
@@ -6107,5 +6108,4 @@ If you find our paper or models helpful, please consider cite as follows:
 
 ## Limitations
 
-Long texts will be truncated to at most 512 tokens.
-
+Long texts will be truncated to at most 512 tokens.