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README.md

@@ -1,4 +1,5 @@
 ---
+library_name: sentence-transformers
 model-index:
 - name: XYZ-embedding-zh
   results:
@@ -10,11 +11,11 @@ model-index:
       type: C-MTEB/CMedQAv1-reranking
     metrics:
     - type: map
-      value: 89.9766367822762
+      value: 89.61792115239176
     - type: mrr
-      value: 91.88896825396824
+      value: 91.46722222222222
     - type: main_score
-      value: 89.9766367822762
+      value: 89.61792115239176
     task:
       type: Reranking
   - dataset:
@@ -25,11 +26,11 @@ model-index:
       type: C-MTEB/CMedQAv2-reranking
     metrics:
     - type: map
-      value: 89.04628340075982
+      value: 89.22040591564271
     - type: mrr
-      value: 91.21702380952381
+      value: 91.2995238095238
     - type: main_score
-      value: 89.04628340075982
+      value: 89.22040591564271
     task:
       type: Reranking
   - dataset:
@@ -40,67 +41,67 @@ model-index:
       type: C-MTEB/CmedqaRetrieval
     metrics:
     - type: map_at_1
-      value: 27.796
+      value: 27.939000000000004
     - type: map_at_10
-      value: 41.498000000000005
+      value: 41.227999999999994
     - type: map_at_100
-      value: 43.332
+      value: 43.018
     - type: map_at_1000
-      value: 43.429
+      value: 43.120000000000005
     - type: map_at_3
-      value: 37.172
+      value: 36.895
     - type: map_at_5
-      value: 39.617000000000004
+      value: 39.373999999999995
     - type: mrr_at_1
-      value: 42.111
+      value: 42.136
     - type: mrr_at_10
-      value: 50.726000000000006
+      value: 50.394000000000005
     - type: mrr_at_100
-      value: 51.632
+      value: 51.288
     - type: mrr_at_1000
-      value: 51.67
+      value: 51.324000000000005
     - type: mrr_at_3
-      value: 48.429
+      value: 47.887
     - type: mrr_at_5
-      value: 49.662
+      value: 49.362
     - type: ndcg_at_1
-      value: 42.111
+      value: 42.136
     - type: ndcg_at_10
-      value: 48.294
+      value: 47.899
     - type: ndcg_at_100
-      value: 55.135999999999996
+      value: 54.730999999999995
     - type: ndcg_at_1000
-      value: 56.818000000000005
+      value: 56.462999999999994
     - type: ndcg_at_3
-      value: 43.185
+      value: 42.66
     - type: ndcg_at_5
-      value: 45.266
+      value: 44.913
     - type: precision_at_1
-      value: 42.111
+      value: 42.136
     - type: precision_at_10
-      value: 10.635
+      value: 10.52
     - type: precision_at_100
-      value: 1.619
+      value: 1.6070000000000002
     - type: precision_at_1000
       value: 0.183
     - type: precision_at_3
-      value: 24.539
+      value: 24.064
     - type: precision_at_5
-      value: 17.644000000000002
+      value: 17.374000000000002
     - type: recall_at_1
-      value: 27.796
+      value: 27.939000000000004
     - type: recall_at_10
-      value: 59.034
+      value: 58.29600000000001
     - type: recall_at_100
-      value: 86.991
+      value: 86.504
     - type: recall_at_1000
-      value: 98.304
+      value: 98.105
     - type: recall_at_3
-      value: 43.356
+      value: 42.475
     - type: recall_at_5
-      value: 49.998
+      value: 49.454
     - type: main_score
-      value: 48.294
+      value: 47.899
     task:
       type: Retrieval
   - dataset:
@@ -111,67 +112,67 @@ model-index:
       type: C-MTEB/CovidRetrieval
     metrics:
     - type: map_at_1
-      value: 80.479
+      value: 77.371
     - type: map_at_10
-      value: 87.984
+      value: 85.229
     - type: map_at_100
-      value: 88.036
+      value: 85.358
     - type: map_at_1000
-      value: 88.03699999999999
+      value: 85.36
     - type: map_at_3
-      value: 87.083
+      value: 84.176
     - type: map_at_5
-      value: 87.694
+      value: 84.79299999999999
     - type: mrr_at_1
-      value: 80.927
+      value: 77.661
     - type: mrr_at_10
-      value: 88.046
+      value: 85.207
     - type: mrr_at_100
-      value: 88.099
+      value: 85.33699999999999
     - type: mrr_at_1000
-      value: 88.1
+      value: 85.339
     - type: mrr_at_3
-      value: 87.215
+      value: 84.229
     - type: mrr_at_5
-      value: 87.768
+      value: 84.79299999999999
     - type: ndcg_at_1
-      value: 80.927
+      value: 77.766
     - type: ndcg_at_10
-      value: 90.756
+      value: 88.237
     - type: ndcg_at_100
-      value: 90.96
+      value: 88.777
     - type: ndcg_at_1000
-      value: 90.975
+      value: 88.818
     - type: ndcg_at_3
-      value: 89.032
+      value: 86.16
     - type: ndcg_at_5
-      value: 90.106
+      value: 87.22
     - type: precision_at_1
-      value: 80.927
+      value: 77.766
     - type: precision_at_10
-      value: 10.011000000000001
+      value: 9.841999999999999
     - type: precision_at_100
-      value: 1.009
+      value: 1.0070000000000001
     - type: precision_at_1000
       value: 0.101
     - type: precision_at_3
-      value: 31.752999999999997
+      value: 30.875000000000004
     - type: precision_at_5
-      value: 19.6
+      value: 19.073
     - type: recall_at_1
-      value: 80.479
+      value: 77.371
     - type: recall_at_10
-      value: 99.05199999999999
+      value: 97.366
     - type: recall_at_100
-      value: 99.895
+      value: 99.684
     - type: recall_at_1000
       value: 100.0
     - type: recall_at_3
-      value: 94.494
+      value: 91.702
     - type: recall_at_5
-      value: 97.102
+      value: 94.31
     - type: main_score
-      value: 90.756
+      value: 88.237
     task:
       type: Retrieval
   - dataset:
@@ -182,67 +183,67 @@ model-index:
       type: C-MTEB/DuRetrieval
     metrics:
     - type: map_at_1
-      value: 27.853
+      value: 27.772000000000002
     - type: map_at_10
-      value: 85.13199999999999
+      value: 84.734
     - type: map_at_100
-      value: 87.688
+      value: 87.298
     - type: map_at_1000
-      value: 87.712
+      value: 87.32900000000001
     - type: map_at_3
-      value: 59.705
+      value: 59.431
     - type: map_at_5
-      value: 75.139
+      value: 74.82900000000001
     - type: mrr_at_1
       value: 93.65
     - type: mrr_at_10
-      value: 95.682
+      value: 95.568
     - type: mrr_at_100
-      value: 95.722
+      value: 95.608
     - type: mrr_at_1000
-      value: 95.724
+      value: 95.609
     - type: mrr_at_3
-      value: 95.467
+      value: 95.267
     - type: mrr_at_5
-      value: 95.612
+      value: 95.494
     - type: ndcg_at_1
       value: 93.65
     - type: ndcg_at_10
-      value: 91.155
+      value: 90.794
     - type: ndcg_at_100
-      value: 93.183
+      value: 92.88300000000001
     - type: ndcg_at_1000
-      value: 93.38499999999999
+      value: 93.144
     - type: ndcg_at_3
-      value: 90.648
+      value: 90.32
     - type: ndcg_at_5
-      value: 89.47699999999999
+      value: 89.242
     - type: precision_at_1
       value: 93.65
     - type: precision_at_10
-      value: 43.11
+      value: 42.9
     - type: precision_at_100
-      value: 4.854
+      value: 4.835
     - type: precision_at_1000
-      value: 0.49100000000000005
+      value: 0.49
     - type: precision_at_3
-      value: 81.11699999999999
+      value: 80.85
     - type: precision_at_5
-      value: 68.28999999999999
+      value: 68.14
     - type: recall_at_1
-      value: 27.853
+      value: 27.772000000000002
     - type: recall_at_10
-      value: 91.678
+      value: 91.183
     - type: recall_at_100
-      value: 98.553
+      value: 98.219
     - type: recall_at_1000
-      value: 99.553
+      value: 99.55000000000001
     - type: recall_at_3
-      value: 61.381
+      value: 60.911
     - type: recall_at_5
-      value: 78.605
+      value: 78.31099999999999
     - type: main_score
-      value: 91.155
+      value: 90.794
     task:
       type: Retrieval
   - dataset:
@@ -253,67 +254,67 @@ model-index:
       type: C-MTEB/EcomRetrieval
     metrics:
     - type: map_at_1
-      value: 54.50000000000001
+      value: 54.6
     - type: map_at_10
-      value: 65.167
+      value: 64.742
     - type: map_at_100
-      value: 65.664
+      value: 65.289
     - type: map_at_1000
-      value: 65.67399999999999
+      value: 65.29700000000001
     - type: map_at_3
-      value: 62.633
+      value: 62.183
     - type: map_at_5
-      value: 64.208
+      value: 63.883
     - type: mrr_at_1
-      value: 54.50000000000001
+      value: 54.6
     - type: mrr_at_10
-      value: 65.167
+      value: 64.742
     - type: mrr_at_100
-      value: 65.664
+      value: 65.289
     - type: mrr_at_1000
-      value: 65.67399999999999
+      value: 65.29700000000001
     - type: mrr_at_3
-      value: 62.633
+      value: 62.183
     - type: mrr_at_5
-      value: 64.208
+      value: 63.883
     - type: ndcg_at_1
-      value: 54.50000000000001
+      value: 54.6
     - type: ndcg_at_10
-      value: 70.294
+      value: 69.719
     - type: ndcg_at_100
-      value: 72.564
+      value: 72.148
     - type: ndcg_at_1000
-      value: 72.841
+      value: 72.393
     - type: ndcg_at_3
-      value: 65.128
+      value: 64.606
     - type: ndcg_at_5
-      value: 67.96799999999999
+      value: 67.682
     - type: precision_at_1
-      value: 54.50000000000001
+      value: 54.6
     - type: precision_at_10
-      value: 8.64
+      value: 8.53
     - type: precision_at_100
-      value: 0.967
+      value: 0.962
     - type: precision_at_1000
-      value: 0.099
+      value: 0.098
     - type: precision_at_3
-      value: 24.099999999999998
+      value: 23.867
     - type: precision_at_5
-      value: 15.840000000000002
+      value: 15.82
     - type: recall_at_1
-      value: 54.50000000000001
+      value: 54.6
     - type: recall_at_10
-      value: 86.4
+      value: 85.3
     - type: recall_at_100
-      value: 96.7
+      value: 96.2
     - type: recall_at_1000
-      value: 98.9
+      value: 98.2
     - type: recall_at_3
-      value: 72.3
+      value: 71.6
     - type: recall_at_5
-      value: 79.2
+      value: 79.10000000000001
     - type: main_score
-      value: 70.294
+      value: 69.719
     task:
       type: Retrieval
   - dataset:
@@ -324,11 +325,11 @@ model-index:
       type: C-MTEB/Mmarco-reranking
     metrics:
     - type: map
-      value: 37.68251937316638
+      value: 35.30260957061897
     - type: mrr
-      value: 36.61746031746032
+      value: 34.098015873015875
     - type: main_score
-      value: 37.68251937316638
+      value: 35.30260957061897
     task:
       type: Reranking
   - dataset:
@@ -339,67 +340,67 @@ model-index:
       type: C-MTEB/MMarcoRetrieval
     metrics:
     - type: map_at_1
-      value: 69.401
+      value: 69.51899999999999
     - type: map_at_10
-      value: 78.8
+      value: 78.816
     - type: map_at_100
-      value: 79.077
+      value: 79.08500000000001
     - type: map_at_1000
-      value: 79.081
+      value: 79.091
     - type: map_at_3
-      value: 76.97
+      value: 76.999
     - type: map_at_5
-      value: 78.185
+      value: 78.194
     - type: mrr_at_1
-      value: 71.719
+      value: 71.80499999999999
     - type: mrr_at_10
-      value: 79.327
+      value: 79.29899999999999
     - type: mrr_at_100
-      value: 79.56400000000001
+      value: 79.532
     - type: mrr_at_1000
-      value: 79.56800000000001
+      value: 79.537
     - type: mrr_at_3
-      value: 77.736
+      value: 77.703
     - type: mrr_at_5
-      value: 78.782
+      value: 78.75999999999999
     - type: ndcg_at_1
-      value: 71.719
+      value: 71.80499999999999
     - type: ndcg_at_10
-      value: 82.505
+      value: 82.479
     - type: ndcg_at_100
-      value: 83.673
+      value: 83.611
     - type: ndcg_at_1000
-      value: 83.786
+      value: 83.76400000000001
     - type: ndcg_at_3
-      value: 79.07600000000001
+      value: 79.065
     - type: ndcg_at_5
-      value: 81.122
+      value: 81.092
     - type: precision_at_1
-      value: 71.719
+      value: 71.80499999999999
     - type: precision_at_10
-      value: 9.924
+      value: 9.91
     - type: precision_at_100
-      value: 1.049
+      value: 1.046
     - type: precision_at_1000
       value: 0.106
     - type: precision_at_3
-      value: 29.742
+      value: 29.727999999999998
     - type: precision_at_5
-      value: 18.937
+      value: 18.908
     - type: recall_at_1
-      value: 69.401
+      value: 69.51899999999999
     - type: recall_at_10
-      value: 93.349
+      value: 93.24
     - type: recall_at_100
-      value: 98.492
+      value: 98.19099999999999
     - type: recall_at_1000
-      value: 99.384
+      value: 99.36500000000001
     - type: recall_at_3
-      value: 84.385
+      value: 84.308
     - type: recall_at_5
-      value: 89.237
+      value: 89.119
     - type: main_score
-      value: 82.505
+      value: 82.479
     task:
       type: Retrieval
   - dataset:
@@ -412,65 +413,65 @@ model-index:
     - type: map_at_1
       value: 57.8
     - type: map_at_10
-      value: 64.696
+      value: 64.215
     - type: map_at_100
-      value: 65.294
+      value: 64.78
     - type: map_at_1000
-      value: 65.328
+      value: 64.81099999999999
     - type: map_at_3
-      value: 62.949999999999996
+      value: 62.64999999999999
     - type: map_at_5
-      value: 64.095
+      value: 63.57000000000001
     - type: mrr_at_1
       value: 58.099999999999994
     - type: mrr_at_10
-      value: 64.85
+      value: 64.371
     - type: mrr_at_100
-      value: 65.448
+      value: 64.936
     - type: mrr_at_1000
-      value: 65.482
+      value: 64.96600000000001
     - type: mrr_at_3
-      value: 63.1
+      value: 62.8
     - type: mrr_at_5
-      value: 64.23
+      value: 63.739999999999995
     - type: ndcg_at_1
       value: 57.8
     - type: ndcg_at_10
-      value: 68.041
+      value: 67.415
     - type: ndcg_at_100
-      value: 71.074
+      value: 70.38799999999999
     - type: ndcg_at_1000
-      value: 71.919
+      value: 71.229
     - type: ndcg_at_3
-      value: 64.584
+      value: 64.206
     - type: ndcg_at_5
-      value: 66.625
+      value: 65.858
     - type: precision_at_1
       value: 57.8
     - type: precision_at_10
-      value: 7.85
+      value: 7.75
     - type: precision_at_100
-      value: 0.9289999999999999
+      value: 0.919
     - type: precision_at_1000
       value: 0.099
     - type: precision_at_3
-      value: 23.1
+      value: 22.900000000000002
     - type: precision_at_5
-      value: 14.84
+      value: 14.540000000000001
     - type: recall_at_1
       value: 57.8
     - type: recall_at_10
-      value: 78.5
+      value: 77.5
     - type: recall_at_100
-      value: 92.9
+      value: 91.9
     - type: recall_at_1000
-      value: 99.4
+      value: 98.6
     - type: recall_at_3
-      value: 69.3
+      value: 68.7
     - type: recall_at_5
-      value: 74.2
+      value: 72.7
     - type: main_score
-      value: 68.041
+      value: 67.415
     task:
       type: Retrieval
   - dataset:
@@ -481,11 +482,11 @@ model-index:
       type: C-MTEB/T2Reranking
     metrics:
     - type: map
-      value: 69.13287570713865
+      value: 69.06615146698508
     - type: mrr
-      value: 79.95326487625066
+      value: 79.7588755091294
     - type: main_score
-      value: 69.13287570713865
+      value: 69.06615146698508
     task:
       type: Reranking
   - dataset:
@@ -496,67 +497,67 @@ model-index:
       type: C-MTEB/T2Retrieval
     metrics:
     - type: map_at_1
-      value: 28.041
+      value: 28.084999999999997
     - type: map_at_10
-      value: 78.509
+      value: 78.583
     - type: map_at_100
-      value: 82.083
+      value: 82.14399999999999
     - type: map_at_1000
-      value: 82.143
+      value: 82.204
     - type: map_at_3
-      value: 55.345
+      value: 55.422000000000004
     - type: map_at_5
-      value: 67.899
+      value: 67.973
     - type: mrr_at_1
-      value: 90.86
+      value: 91.014
     - type: mrr_at_10
-      value: 93.31
+      value: 93.381
     - type: mrr_at_100
-      value: 93.388
+      value: 93.45400000000001
     - type: mrr_at_1000
-      value: 93.391
+      value: 93.45599999999999
     - type: mrr_at_3
-      value: 92.92200000000001
+      value: 92.99300000000001
     - type: mrr_at_5
-      value: 93.167
+      value: 93.234
     - type: ndcg_at_1
-      value: 90.86
+      value: 91.014
     - type: ndcg_at_10
-      value: 85.875
+      value: 85.931
     - type: ndcg_at_100
-      value: 89.269
+      value: 89.31
     - type: ndcg_at_1000
-      value: 89.827
+      value: 89.869
     - type: ndcg_at_3
-      value: 87.254
+      value: 87.348
     - type: ndcg_at_5
-      value: 85.855
+      value: 85.929
     - type: precision_at_1
-      value: 90.86
+      value: 91.014
     - type: precision_at_10
-      value: 42.488
+      value: 42.495
     - type: precision_at_100
-      value: 5.029
+      value: 5.029999999999999
     - type: precision_at_1000
       value: 0.516
     - type: precision_at_3
-      value: 76.172
+      value: 76.248
     - type: precision_at_5
-      value: 63.759
+      value: 63.817
     - type: recall_at_1
-      value: 28.041
+      value: 28.084999999999997
     - type: recall_at_10
-      value: 84.829
+      value: 84.88
     - type: recall_at_100
-      value: 95.89999999999999
+      value: 95.902
     - type: recall_at_1000
-      value: 98.665
+      value: 98.699
     - type: recall_at_3
-      value: 57.009
+      value: 57.113
     - type: recall_at_5
-      value: 71.188
+      value: 71.251
     - type: main_score
-      value: 85.875
+      value: 85.931
     task:
       type: Retrieval
   - dataset:
@@ -567,99 +568,121 @@ model-index:
       type: C-MTEB/VideoRetrieval
     metrics:
     - type: map_at_1
-      value: 67.30000000000001
+      value: 66.4
     - type: map_at_10
-      value: 76.819
+      value: 75.86
     - type: map_at_100
-      value: 77.141
+      value: 76.185
     - type: map_at_1000
-      value: 77.142
+      value: 76.188
     - type: map_at_3
-      value: 75.233
+      value: 74.167
     - type: map_at_5
-      value: 76.163
+      value: 75.187
     - type: mrr_at_1
-      value: 67.30000000000001
+      value: 66.4
     - type: mrr_at_10
-      value: 76.819
+      value: 75.86
     - type: mrr_at_100
-      value: 77.141
+      value: 76.185
     - type: mrr_at_1000
-      value: 77.142
+      value: 76.188
     - type: mrr_at_3
-      value: 75.233
+      value: 74.167
     - type: mrr_at_5
-      value: 76.163
+      value: 75.187
     - type: ndcg_at_1
-      value: 67.30000000000001
+      value: 66.4
     - type: ndcg_at_10
-      value: 80.93599999999999
+      value: 80.03099999999999
     - type: ndcg_at_100
-      value: 82.311
+      value: 81.459
     - type: ndcg_at_1000
-      value: 82.349
+      value: 81.527
     - type: ndcg_at_3
-      value: 77.724
+      value: 76.621
     - type: ndcg_at_5
-      value: 79.406
+      value: 78.446
     - type: precision_at_1
-      value: 67.30000000000001
+      value: 66.4
     - type: precision_at_10
-      value: 9.36
+      value: 9.29
     - type: precision_at_100
-      value: 0.996
+      value: 0.992
     - type: precision_at_1000
       value: 0.1
     - type: precision_at_3
-      value: 28.299999999999997
+      value: 27.900000000000002
     - type: precision_at_5
-      value: 17.8
+      value: 17.62
     - type: recall_at_1
-      value: 67.30000000000001
+      value: 66.4
     - type: recall_at_10
-      value: 93.60000000000001
+      value: 92.9
     - type: recall_at_100
-      value: 99.6
+      value: 99.2
     - type: recall_at_1000
-      value: 99.9
+      value: 99.7
     - type: recall_at_3
-      value: 84.89999999999999
+      value: 83.7
     - type: recall_at_5
-      value: 89.0
+      value: 88.1
     - type: main_score
-      value: 80.93599999999999
+      value: 80.03099999999999
     task:
       type: Retrieval
+pipeline_tag: sentence-similarity
 tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
 - mteb
 ---
-XYZ-embedding-zh
+-
+
+# XYZ-embedding-zh
 
-## Usage (Sentence Transformers)
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1792 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-First install the Sentence Transformers library:
+<!--- Describe your model here -->
 
-```bash
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
 pip install -U sentence-transformers
 ```
-Then you can load this model and run inference.
+
+Then you can use the model like this:
+
 ```python
 from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
 
-# Download from the 🤗 Hub
-model = SentenceTransformer("fangxq/XYZ-embedding-zh")
-# Run inference
-sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
-]
+model = SentenceTransformer('fangxq/XYZ-embedding-zh')
 embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 1792]
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- 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={MODEL_NAME})
 
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Dense({'in_features': 1024, 'out_features': 1792, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
 ```
+
+## Citing & Authors
+