Amu commited on
Commit
11da0d9
1 Parent(s): 54df0d9

[update] model

Browse files
Files changed (5) hide show
  1. README.md +363 -363
  2. config.json +34 -34
  3. pytorch_model.bin +2 -2
  4. special_tokens_map.json +0 -7
  5. tokenizer_config.json +54 -60
README.md CHANGED
@@ -6,7 +6,7 @@ tags:
6
  - sentence-similarity
7
  - mteb
8
  model-index:
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- - name: tao-8k
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  results:
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  - task:
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  type: STS
@@ -18,17 +18,17 @@ model-index:
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  revision: None
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  metrics:
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  - type: cos_sim_pearson
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- value: 46.6327281304144
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  - type: cos_sim_spearman
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- value: 48.842454434123376
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  - type: euclidean_pearson
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- value: 46.94481399008005
26
  - type: euclidean_spearman
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- value: 48.842454434123376
28
  - type: manhattan_pearson
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- value: 46.89375935801324
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  - type: manhattan_spearman
31
- value: 48.78990181105918
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  - task:
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  type: STS
34
  dataset:
@@ -39,17 +39,17 @@ model-index:
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  revision: None
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  metrics:
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  - type: cos_sim_pearson
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- value: 51.29442837260785
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  - type: cos_sim_spearman
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- value: 52.652094634834
45
  - type: euclidean_pearson
46
- value: 54.86278112546793
47
  - type: euclidean_spearman
48
- value: 52.65209238258423
49
  - type: manhattan_pearson
50
- value: 54.8164800665497
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  - type: manhattan_spearman
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- value: 52.626711935726014
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  - task:
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  type: Classification
55
  dataset:
@@ -60,9 +60,9 @@ model-index:
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  revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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  metrics:
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  - type: accuracy
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- value: 41.51200000000001
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  - type: f1
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- value: 39.47955832883091
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  - task:
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  type: STS
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  dataset:
@@ -73,17 +73,17 @@ model-index:
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  revision: None
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  metrics:
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  - type: cos_sim_pearson
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- value: 63.27653562193512
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  - type: cos_sim_spearman
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- value: 65.37293598647585
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  - type: euclidean_pearson
80
- value: 63.91367659963474
81
  - type: euclidean_spearman
82
- value: 65.37294637878077
83
  - type: manhattan_pearson
84
- value: 63.89671277983551
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  - type: manhattan_spearman
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- value: 65.35510625635355
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  - task:
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  type: Clustering
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  dataset:
@@ -94,7 +94,7 @@ model-index:
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  revision: None
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  metrics:
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  - type: v_measure
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- value: 39.92148459596857
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  - task:
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  type: Clustering
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  dataset:
@@ -105,7 +105,7 @@ model-index:
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  revision: None
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  metrics:
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  - type: v_measure
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- value: 36.7800929733979
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  - task:
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  type: Reranking
111
  dataset:
@@ -116,9 +116,9 @@ model-index:
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  revision: None
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  metrics:
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  - type: map
119
- value: 84.56370955233704
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  - type: mrr
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- value: 87.14396825396825
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  - task:
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  type: Reranking
124
  dataset:
@@ -129,9 +129,9 @@ model-index:
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  revision: None
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  metrics:
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  - type: map
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- value: 85.4719112626303
133
  - type: mrr
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- value: 88.25107142857142
135
  - task:
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  type: Retrieval
137
  dataset:
@@ -142,65 +142,65 @@ model-index:
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  revision: None
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  metrics:
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  - type: map_at_1
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- value: 24.314
146
  - type: map_at_10
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- value: 36.157000000000004
148
  - type: map_at_100
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- value: 38.004
150
  - type: map_at_1000
151
- value: 38.129999999999995
152
  - type: map_at_3
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- value: 32.141999999999996
154
  - type: map_at_5
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- value: 34.414
156
  - type: mrr_at_1
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- value: 37.384
158
  - type: mrr_at_10
159
- value: 45.261
160
  - type: mrr_at_100
161
- value: 46.271
162
  - type: mrr_at_1000
163
- value: 46.32
164
  - type: mrr_at_3
165
- value: 42.760999999999996
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  - type: mrr_at_5
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- value: 44.219
168
  - type: ndcg_at_1
169
- value: 37.384
170
  - type: ndcg_at_10
171
- value: 42.599
172
  - type: ndcg_at_100
173
- value: 50.068999999999996
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  - type: ndcg_at_1000
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- value: 52.221
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  - type: ndcg_at_3
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- value: 37.551
178
  - type: ndcg_at_5
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- value: 39.711
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  - type: precision_at_1
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- value: 37.384
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  - type: precision_at_10
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- value: 9.532
184
  - type: precision_at_100
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- value: 1.554
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  - type: precision_at_1000
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  value: 0.183
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  - type: precision_at_3
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- value: 21.205
190
  - type: precision_at_5
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- value: 15.539
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  - type: recall_at_1
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- value: 24.314
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  - type: recall_at_10
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- value: 52.463
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  - type: recall_at_100
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- value: 83.86099999999999
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  - type: recall_at_1000
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- value: 98.17399999999999
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  - type: recall_at_3
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- value: 37.341
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  - type: recall_at_5
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- value: 43.952999999999996
204
  - task:
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  type: PairClassification
206
  dataset:
@@ -211,51 +211,51 @@ model-index:
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  revision: None
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  metrics:
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  - type: cos_sim_accuracy
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- value: 78.80938063740228
215
  - type: cos_sim_ap
216
- value: 87.42519095434638
217
  - type: cos_sim_f1
218
- value: 80.08597528210638
219
  - type: cos_sim_precision
220
- value: 74.10501193317423
221
  - type: cos_sim_recall
222
- value: 87.11713818096797
223
  - type: dot_accuracy
224
- value: 78.80938063740228
225
  - type: dot_ap
226
- value: 87.44023261310717
227
  - type: dot_f1
228
- value: 80.08597528210638
229
  - type: dot_precision
230
- value: 74.10501193317423
231
  - type: dot_recall
232
- value: 87.11713818096797
233
  - type: euclidean_accuracy
234
- value: 78.80938063740228
235
  - type: euclidean_ap
236
- value: 87.42517285949802
237
  - type: euclidean_f1
238
- value: 80.08597528210638
239
  - type: euclidean_precision
240
- value: 74.10501193317423
241
  - type: euclidean_recall
242
- value: 87.11713818096797
243
  - type: manhattan_accuracy
244
- value: 78.90559230306675
245
  - type: manhattan_ap
246
- value: 87.38730802838026
247
  - type: manhattan_f1
248
- value: 80.1043138107139
249
  - type: manhattan_precision
250
- value: 74.82744620381648
251
  - type: manhattan_recall
252
- value: 86.1819032031798
253
  - type: max_accuracy
254
- value: 78.90559230306675
255
  - type: max_ap
256
- value: 87.44023261310717
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  - type: max_f1
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- value: 80.1043138107139
259
  - task:
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  type: Retrieval
261
  dataset:
@@ -266,65 +266,65 @@ model-index:
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  revision: None
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  metrics:
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  - type: map_at_1
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- value: 69.863
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  - type: map_at_10
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- value: 77.865
272
  - type: map_at_100
273
- value: 78.21900000000001
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  - type: map_at_1000
275
- value: 78.22200000000001
276
  - type: map_at_3
277
- value: 76.335
278
  - type: map_at_5
279
- value: 77.179
280
  - type: mrr_at_1
281
- value: 70.074
282
  - type: mrr_at_10
283
- value: 77.89
284
  - type: mrr_at_100
285
- value: 78.235
286
  - type: mrr_at_1000
287
- value: 78.238
288
  - type: mrr_at_3
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- value: 76.466
290
  - type: mrr_at_5
291
- value: 77.241
292
  - type: ndcg_at_1
293
- value: 70.074
294
  - type: ndcg_at_10
295
- value: 81.375
296
  - type: ndcg_at_100
297
- value: 82.918
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  - type: ndcg_at_1000
299
- value: 83.019
300
  - type: ndcg_at_3
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- value: 78.32000000000001
302
  - type: ndcg_at_5
303
- value: 79.824
304
  - type: precision_at_1
305
- value: 70.074
306
  - type: precision_at_10
307
- value: 9.325999999999999
308
  - type: precision_at_100
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- value: 1.001
310
  - type: precision_at_1000
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  value: 0.101
312
  - type: precision_at_3
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- value: 28.17
314
  - type: precision_at_5
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- value: 17.682000000000002
316
  - type: recall_at_1
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- value: 69.863
318
  - type: recall_at_10
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- value: 92.202
320
  - type: recall_at_100
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- value: 99.05199999999999
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  - type: recall_at_1000
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- value: 99.895
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  - type: recall_at_3
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- value: 83.93
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  - type: recall_at_5
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- value: 87.566
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  - task:
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  type: Retrieval
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  dataset:
@@ -335,65 +335,65 @@ model-index:
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  revision: None
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  metrics:
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  - type: map_at_1
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- value: 25.730999999999998
339
  - type: map_at_10
340
- value: 80.765
341
  - type: map_at_100
342
- value: 83.486
343
  - type: map_at_1000
344
- value: 83.521
345
  - type: map_at_3
346
- value: 55.745999999999995
347
  - type: map_at_5
348
- value: 70.473
349
  - type: mrr_at_1
350
- value: 89.55
351
  - type: mrr_at_10
352
- value: 93.028
353
  - type: mrr_at_100
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- value: 93.093
355
  - type: mrr_at_1000
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- value: 93.096
357
  - type: mrr_at_3
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- value: 92.80000000000001
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  - type: mrr_at_5
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- value: 92.92200000000001
361
  - type: ndcg_at_1
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- value: 89.55
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  - type: ndcg_at_10
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- value: 87.898
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  - type: ndcg_at_100
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- value: 90.366
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  - type: ndcg_at_1000
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- value: 90.715
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  - type: ndcg_at_3
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- value: 86.497
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  - type: ndcg_at_5
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- value: 85.533
373
  - type: precision_at_1
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- value: 89.55
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  - type: precision_at_10
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- value: 42.305
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  - type: precision_at_100
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- value: 4.82
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  - type: precision_at_1000
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  value: 0.48900000000000005
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  - type: precision_at_3
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- value: 77.833
383
  - type: precision_at_5
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- value: 65.81
385
  - type: recall_at_1
386
- value: 25.730999999999998
387
  - type: recall_at_10
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- value: 89.409
389
  - type: recall_at_100
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- value: 97.62100000000001
391
  - type: recall_at_1000
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- value: 99.565
393
  - type: recall_at_3
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- value: 58.298
395
  - type: recall_at_5
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- value: 75.315
397
  - task:
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  type: Retrieval
399
  dataset:
@@ -404,65 +404,65 @@ model-index:
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  revision: None
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  metrics:
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  - type: map_at_1
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- value: 49.6
408
  - type: map_at_10
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- value: 59.34
410
  - type: map_at_100
411
- value: 59.894999999999996
412
  - type: map_at_1000
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- value: 59.913000000000004
414
  - type: map_at_3
415
- value: 56.667
416
  - type: map_at_5
417
- value: 58.196999999999996
418
  - type: mrr_at_1
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- value: 49.6
420
  - type: mrr_at_10
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- value: 59.34
422
  - type: mrr_at_100
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- value: 59.894999999999996
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  - type: mrr_at_1000
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- value: 59.913000000000004
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  - type: mrr_at_3
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- value: 56.667
428
  - type: mrr_at_5
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- value: 58.196999999999996
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  - type: ndcg_at_1
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- value: 49.6
432
  - type: ndcg_at_10
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- value: 64.461
434
  - type: ndcg_at_100
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- value: 67.08800000000001
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  - type: ndcg_at_1000
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- value: 67.578
438
  - type: ndcg_at_3
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- value: 58.962
440
  - type: ndcg_at_5
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- value: 61.741
442
  - type: precision_at_1
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- value: 49.6
444
  - type: precision_at_10
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- value: 8.07
446
  - type: precision_at_100
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- value: 0.928
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  - type: precision_at_1000
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- value: 0.097
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  - type: precision_at_3
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- value: 21.867
452
  - type: precision_at_5
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- value: 14.48
454
  - type: recall_at_1
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- value: 49.6
456
  - type: recall_at_10
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- value: 80.7
458
  - type: recall_at_100
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- value: 92.80000000000001
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  - type: recall_at_1000
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- value: 96.7
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  - type: recall_at_3
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- value: 65.60000000000001
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  - type: recall_at_5
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- value: 72.39999999999999
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  - task:
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  type: Classification
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  dataset:
@@ -473,9 +473,9 @@ model-index:
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  revision: None
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  metrics:
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  - type: accuracy
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- value: 47.44132358599462
477
  - type: f1
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- value: 34.814352930577854
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  - task:
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  type: Classification
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  dataset:
@@ -486,11 +486,11 @@ model-index:
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  revision: None
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  metrics:
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  - type: accuracy
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- value: 86.43527204502813
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  - type: ap
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- value: 55.197728692877554
492
  - type: f1
493
- value: 81.22331922899193
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  - task:
495
  type: STS
496
  dataset:
@@ -501,17 +501,17 @@ model-index:
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  revision: None
502
  metrics:
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  - type: cos_sim_pearson
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- value: 72.21054197899034
505
  - type: cos_sim_spearman
506
- value: 77.10172371889475
507
  - type: euclidean_pearson
508
- value: 76.15914782847307
509
  - type: euclidean_spearman
510
- value: 77.10173036795658
511
  - type: manhattan_pearson
512
- value: 76.16257390318928
513
  - type: manhattan_spearman
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- value: 77.10538180843567
515
  - task:
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  type: Reranking
517
  dataset:
@@ -522,9 +522,9 @@ model-index:
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  revision: None
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  metrics:
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  - type: map
525
- value: 26.968179320629726
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  - type: mrr
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- value: 25.664285714285718
528
  - task:
529
  type: Retrieval
530
  dataset:
@@ -535,65 +535,65 @@ model-index:
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  revision: None
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  metrics:
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  - type: map_at_1
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- value: 66.674
539
  - type: map_at_10
540
- value: 75.624
541
  - type: map_at_100
542
- value: 75.96199999999999
543
  - type: map_at_1000
544
- value: 75.973
545
  - type: map_at_3
546
- value: 73.9
547
  - type: map_at_5
548
- value: 75.007
549
  - type: mrr_at_1
550
- value: 68.89699999999999
551
  - type: mrr_at_10
552
- value: 76.212
553
  - type: mrr_at_100
554
- value: 76.506
555
  - type: mrr_at_1000
556
- value: 76.517
557
  - type: mrr_at_3
558
- value: 74.72999999999999
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  - type: mrr_at_5
560
- value: 75.65899999999999
561
  - type: ndcg_at_1
562
- value: 68.89699999999999
563
  - type: ndcg_at_10
564
- value: 79.19
565
  - type: ndcg_at_100
566
- value: 80.681
567
  - type: ndcg_at_1000
568
- value: 80.97999999999999
569
  - type: ndcg_at_3
570
- value: 75.954
571
  - type: ndcg_at_5
572
- value: 77.792
573
  - type: precision_at_1
574
- value: 68.89699999999999
575
  - type: precision_at_10
576
- value: 9.519
577
  - type: precision_at_100
578
- value: 1.026
579
  - type: precision_at_1000
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  value: 0.105
581
  - type: precision_at_3
582
- value: 28.548000000000002
583
  - type: precision_at_5
584
- value: 18.117
585
  - type: recall_at_1
586
- value: 66.674
587
  - type: recall_at_10
588
- value: 89.55499999999999
589
  - type: recall_at_100
590
- value: 96.26
591
  - type: recall_at_1000
592
- value: 98.598
593
  - type: recall_at_3
594
- value: 81.029
595
  - type: recall_at_5
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- value: 85.37700000000001
597
  - task:
598
  type: Classification
599
  dataset:
@@ -604,9 +604,9 @@ model-index:
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  revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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  metrics:
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  - type: accuracy
607
- value: 68.13718897108271
608
  - type: f1
609
- value: 66.00508413016382
610
  - task:
611
  type: Classification
612
  dataset:
@@ -617,9 +617,9 @@ model-index:
617
  revision: 7d571f92784cd94a019292a1f45445077d0ef634
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  metrics:
619
  - type: accuracy
620
- value: 72.542030934768
621
  - type: f1
622
- value: 71.87970959109703
623
  - task:
624
  type: Retrieval
625
  dataset:
@@ -630,65 +630,65 @@ model-index:
630
  revision: None
631
  metrics:
632
  - type: map_at_1
633
- value: 51.2
634
  - type: map_at_10
635
- value: 57.211999999999996
636
  - type: map_at_100
637
- value: 57.74
638
  - type: map_at_1000
639
- value: 57.791000000000004
640
  - type: map_at_3
641
- value: 55.900000000000006
642
  - type: map_at_5
643
- value: 56.665
644
  - type: mrr_at_1
645
- value: 51.300000000000004
646
  - type: mrr_at_10
647
- value: 57.252
648
  - type: mrr_at_100
649
- value: 57.789
650
  - type: mrr_at_1000
651
- value: 57.84
652
  - type: mrr_at_3
653
- value: 55.95
654
  - type: mrr_at_5
655
- value: 56.715
656
  - type: ndcg_at_1
657
- value: 51.2
658
  - type: ndcg_at_10
659
- value: 59.998
660
  - type: ndcg_at_100
661
- value: 62.971999999999994
662
  - type: ndcg_at_1000
663
- value: 64.453
664
  - type: ndcg_at_3
665
- value: 57.321
666
  - type: ndcg_at_5
667
- value: 58.711
668
  - type: precision_at_1
669
- value: 51.2
670
  - type: precision_at_10
671
- value: 6.87
672
  - type: precision_at_100
673
- value: 0.835
674
  - type: precision_at_1000
675
- value: 0.095
676
  - type: precision_at_3
677
- value: 20.467
678
  - type: precision_at_5
679
- value: 12.959999999999999
680
  - type: recall_at_1
681
- value: 51.2
682
  - type: recall_at_10
683
- value: 68.7
684
  - type: recall_at_100
685
- value: 83.5
686
  - type: recall_at_1000
687
- value: 95.39999999999999
688
  - type: recall_at_3
689
- value: 61.4
690
  - type: recall_at_5
691
- value: 64.8
692
  - task:
693
  type: Classification
694
  dataset:
@@ -699,9 +699,9 @@ model-index:
699
  revision: None
700
  metrics:
701
  - type: accuracy
702
- value: 73.33000000000001
703
  - type: f1
704
- value: 72.76740880461465
705
  - task:
706
  type: PairClassification
707
  dataset:
@@ -712,51 +712,51 @@ model-index:
712
  revision: None
713
  metrics:
714
  - type: cos_sim_accuracy
715
- value: 75.09474824038982
716
  - type: cos_sim_ap
717
- value: 79.49093167837522
718
  - type: cos_sim_f1
719
- value: 77.762619372442
720
  - type: cos_sim_precision
721
- value: 68.29073482428115
722
  - type: cos_sim_recall
723
- value: 90.28511087645195
724
  - type: dot_accuracy
725
- value: 75.09474824038982
726
  - type: dot_ap
727
- value: 79.49093167837522
728
  - type: dot_f1
729
- value: 77.762619372442
730
  - type: dot_precision
731
- value: 68.29073482428115
732
  - type: dot_recall
733
- value: 90.28511087645195
734
  - type: euclidean_accuracy
735
- value: 75.09474824038982
736
  - type: euclidean_ap
737
- value: 79.49093167837522
738
  - type: euclidean_f1
739
- value: 77.762619372442
740
  - type: euclidean_precision
741
- value: 68.29073482428115
742
  - type: euclidean_recall
743
- value: 90.28511087645195
744
  - type: manhattan_accuracy
745
- value: 74.93232268543584
746
  - type: manhattan_ap
747
- value: 79.50256779527038
748
  - type: manhattan_f1
749
- value: 77.3749426342359
750
  - type: manhattan_precision
751
- value: 68.42532467532467
752
  - type: manhattan_recall
753
- value: 89.01795142555439
754
  - type: max_accuracy
755
- value: 75.09474824038982
756
  - type: max_ap
757
- value: 79.50256779527038
758
  - type: max_f1
759
- value: 77.762619372442
760
  - task:
761
  type: Classification
762
  dataset:
@@ -767,11 +767,11 @@ model-index:
767
  revision: None
768
  metrics:
769
  - type: accuracy
770
- value: 91.71
771
  - type: ap
772
- value: 89.30664330630684
773
  - type: f1
774
- value: 91.69380669543091
775
  - task:
776
  type: STS
777
  dataset:
@@ -782,17 +782,17 @@ model-index:
782
  revision: None
783
  metrics:
784
  - type: cos_sim_pearson
785
- value: 27.87844586552044
786
  - type: cos_sim_spearman
787
- value: 33.55828345961726
788
  - type: euclidean_pearson
789
- value: 34.008422591348754
790
  - type: euclidean_spearman
791
- value: 33.55828173553759
792
  - type: manhattan_pearson
793
- value: 33.97354762221951
794
  - type: manhattan_spearman
795
- value: 33.55061748217219
796
  - task:
797
  type: STS
798
  dataset:
@@ -803,17 +803,17 @@ model-index:
803
  revision: None
804
  metrics:
805
  - type: cos_sim_pearson
806
- value: 37.16475906990342
807
  - type: cos_sim_spearman
808
- value: 39.02023124990304
809
  - type: euclidean_pearson
810
- value: 37.12905621621282
811
  - type: euclidean_spearman
812
- value: 39.02017798495793
813
  - type: manhattan_pearson
814
- value: 37.16400100601629
815
  - type: manhattan_spearman
816
- value: 39.027383935772335
817
  - task:
818
  type: STS
819
  dataset:
@@ -824,17 +824,17 @@ model-index:
824
  revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
825
  metrics:
826
  - type: cos_sim_pearson
827
- value: 66.7431509369159
828
  - type: cos_sim_spearman
829
- value: 69.10355047922879
830
  - type: euclidean_pearson
831
- value: 67.48723360063258
832
  - type: euclidean_spearman
833
- value: 69.10355047922879
834
  - type: manhattan_pearson
835
- value: 67.55981324291854
836
  - type: manhattan_spearman
837
- value: 69.1816947077302
838
  - task:
839
  type: STS
840
  dataset:
@@ -845,17 +845,17 @@ model-index:
845
  revision: None
846
  metrics:
847
  - type: cos_sim_pearson
848
- value: 78.27412453529412
849
  - type: cos_sim_spearman
850
- value: 78.74292565872022
851
  - type: euclidean_pearson
852
- value: 77.95359390335884
853
  - type: euclidean_spearman
854
- value: 78.7428438579602
855
  - type: manhattan_pearson
856
- value: 77.99252788851469
857
  - type: manhattan_spearman
858
- value: 78.80401873296358
859
  - task:
860
  type: Reranking
861
  dataset:
@@ -866,9 +866,9 @@ model-index:
866
  revision: None
867
  metrics:
868
  - type: map
869
- value: 66.42334440897298
870
  - type: mrr
871
- value: 76.24570128209263
872
  - task:
873
  type: Retrieval
874
  dataset:
@@ -879,65 +879,65 @@ model-index:
879
  revision: None
880
  metrics:
881
  - type: map_at_1
882
- value: 27.323999999999998
883
  - type: map_at_10
884
- value: 76.752
885
  - type: map_at_100
886
- value: 80.39
887
  - type: map_at_1000
888
- value: 80.457
889
  - type: map_at_3
890
- value: 53.93
891
  - type: map_at_5
892
- value: 66.263
893
  - type: mrr_at_1
894
- value: 89.90899999999999
895
  - type: mrr_at_10
896
- value: 92.35
897
  - type: mrr_at_100
898
- value: 92.43599999999999
899
  - type: mrr_at_1000
900
- value: 92.44
901
  - type: mrr_at_3
902
- value: 91.92
903
  - type: mrr_at_5
904
- value: 92.192
905
  - type: ndcg_at_1
906
- value: 89.90899999999999
907
  - type: ndcg_at_10
908
- value: 84.352
909
  - type: ndcg_at_100
910
- value: 87.978
911
  - type: ndcg_at_1000
912
- value: 88.631
913
  - type: ndcg_at_3
914
- value: 85.845
915
  - type: ndcg_at_5
916
- value: 84.35000000000001
917
  - type: precision_at_1
918
- value: 89.90899999999999
919
  - type: precision_at_10
920
- value: 41.985
921
  - type: precision_at_100
922
- value: 5.007000000000001
923
  - type: precision_at_1000
924
  value: 0.516
925
  - type: precision_at_3
926
- value: 75.146
927
  - type: precision_at_5
928
- value: 62.92100000000001
929
  - type: recall_at_1
930
- value: 27.323999999999998
931
  - type: recall_at_10
932
- value: 83.221
933
  - type: recall_at_100
934
- value: 95.088
935
  - type: recall_at_1000
936
- value: 98.436
937
  - type: recall_at_3
938
- value: 55.58
939
  - type: recall_at_5
940
- value: 69.594
941
  - task:
942
  type: Classification
943
  dataset:
@@ -948,9 +948,9 @@ model-index:
948
  revision: None
949
  metrics:
950
  - type: accuracy
951
- value: 50.453
952
  - type: f1
953
- value: 48.736715267813835
954
  - task:
955
  type: Clustering
956
  dataset:
@@ -961,7 +961,7 @@ model-index:
961
  revision: None
962
  metrics:
963
  - type: v_measure
964
- value: 59.153574405500706
965
  - task:
966
  type: Clustering
967
  dataset:
@@ -972,7 +972,7 @@ model-index:
972
  revision: None
973
  metrics:
974
  - type: v_measure
975
- value: 52.79421409479782
976
  - task:
977
  type: Retrieval
978
  dataset:
@@ -983,65 +983,65 @@ model-index:
983
  revision: None
984
  metrics:
985
  - type: map_at_1
986
- value: 56.699999999999996
987
  - type: map_at_10
988
- value: 66.834
989
  - type: map_at_100
990
- value: 67.313
991
  - type: map_at_1000
992
- value: 67.325
993
  - type: map_at_3
994
- value: 65.017
995
  - type: map_at_5
996
- value: 65.927
997
  - type: mrr_at_1
998
- value: 56.699999999999996
999
  - type: mrr_at_10
1000
- value: 66.834
1001
  - type: mrr_at_100
1002
- value: 67.313
1003
  - type: mrr_at_1000
1004
- value: 67.325
1005
  - type: mrr_at_3
1006
- value: 65.017
1007
  - type: mrr_at_5
1008
- value: 65.927
1009
  - type: ndcg_at_1
1010
- value: 56.699999999999996
1011
  - type: ndcg_at_10
1012
- value: 71.576
1013
  - type: ndcg_at_100
1014
- value: 73.79400000000001
1015
  - type: ndcg_at_1000
1016
- value: 74.08200000000001
1017
  - type: ndcg_at_3
1018
- value: 67.73400000000001
1019
  - type: ndcg_at_5
1020
- value: 69.378
1021
  - type: precision_at_1
1022
- value: 56.699999999999996
1023
  - type: precision_at_10
1024
- value: 8.64
1025
  - type: precision_at_100
1026
- value: 0.9650000000000001
1027
  - type: precision_at_1000
1028
- value: 0.099
1029
  - type: precision_at_3
1030
- value: 25.2
1031
  - type: precision_at_5
1032
- value: 15.920000000000002
1033
  - type: recall_at_1
1034
- value: 56.699999999999996
1035
  - type: recall_at_10
1036
- value: 86.4
1037
  - type: recall_at_100
1038
- value: 96.5
1039
  - type: recall_at_1000
1040
- value: 98.7
1041
  - type: recall_at_3
1042
- value: 75.6
1043
  - type: recall_at_5
1044
- value: 79.60000000000001
1045
  - task:
1046
  type: Classification
1047
  dataset:
@@ -1052,11 +1052,11 @@ model-index:
1052
  revision: None
1053
  metrics:
1054
  - type: accuracy
1055
- value: 86.83
1056
  - type: ap
1057
- value: 70.2908139255317
1058
  - type: f1
1059
- value: 85.19267443803346
1060
  ---
1061
 
1062
  a try for emebdding model
 
6
  - sentence-similarity
7
  - mteb
8
  model-index:
9
+ - name: tao
10
  results:
11
  - task:
12
  type: STS
 
18
  revision: None
19
  metrics:
20
  - type: cos_sim_pearson
21
+ value: 47.33752515292192
22
  - type: cos_sim_spearman
23
+ value: 49.940772056837176
24
  - type: euclidean_pearson
25
+ value: 48.12147487857213
26
  - type: euclidean_spearman
27
+ value: 49.9407519488174
28
  - type: manhattan_pearson
29
+ value: 48.07550286372865
30
  - type: manhattan_spearman
31
+ value: 49.89535645392862
32
  - task:
33
  type: STS
34
  dataset:
 
39
  revision: None
40
  metrics:
41
  - type: cos_sim_pearson
42
+ value: 50.976865711125626
43
  - type: cos_sim_spearman
44
+ value: 53.113084748593465
45
  - type: euclidean_pearson
46
+ value: 55.1209592747571
47
  - type: euclidean_spearman
48
+ value: 53.11308362230699
49
  - type: manhattan_pearson
50
+ value: 55.09799309322416
51
  - type: manhattan_spearman
52
+ value: 53.108059998577076
53
  - task:
54
  type: Classification
55
  dataset:
 
60
  revision: 1399c76144fd37290681b995c656ef9b2e06e26d
61
  metrics:
62
  - type: accuracy
63
+ value: 40.812
64
  - type: f1
65
+ value: 39.02060856097395
66
  - task:
67
  type: STS
68
  dataset:
 
73
  revision: None
74
  metrics:
75
  - type: cos_sim_pearson
76
+ value: 62.84336868097746
77
  - type: cos_sim_spearman
78
+ value: 65.540605433497
79
  - type: euclidean_pearson
80
+ value: 64.08759819387913
81
  - type: euclidean_spearman
82
+ value: 65.54060543369363
83
  - type: manhattan_pearson
84
+ value: 64.09334283385029
85
  - type: manhattan_spearman
86
+ value: 65.55376209169398
87
  - task:
88
  type: Clustering
89
  dataset:
 
94
  revision: None
95
  metrics:
96
  - type: v_measure
97
+ value: 39.964020691388505
98
  - task:
99
  type: Clustering
100
  dataset:
 
105
  revision: None
106
  metrics:
107
  - type: v_measure
108
+ value: 38.18628830038994
109
  - task:
110
  type: Reranking
111
  dataset:
 
116
  revision: None
117
  metrics:
118
  - type: map
119
+ value: 85.34294439514511
120
  - type: mrr
121
+ value: 88.03849206349206
122
  - task:
123
  type: Reranking
124
  dataset:
 
129
  revision: None
130
  metrics:
131
  - type: map
132
+ value: 85.87127698007234
133
  - type: mrr
134
+ value: 88.57980158730159
135
  - task:
136
  type: Retrieval
137
  dataset:
 
142
  revision: None
143
  metrics:
144
  - type: map_at_1
145
+ value: 24.484
146
  - type: map_at_10
147
+ value: 36.3
148
  - type: map_at_100
149
+ value: 38.181
150
  - type: map_at_1000
151
+ value: 38.305
152
  - type: map_at_3
153
+ value: 32.39
154
  - type: map_at_5
155
+ value: 34.504000000000005
156
  - type: mrr_at_1
157
+ value: 37.608999999999995
158
  - type: mrr_at_10
159
+ value: 45.348
160
  - type: mrr_at_100
161
+ value: 46.375
162
  - type: mrr_at_1000
163
+ value: 46.425
164
  - type: mrr_at_3
165
+ value: 42.969
166
  - type: mrr_at_5
167
+ value: 44.285999999999994
168
  - type: ndcg_at_1
169
+ value: 37.608999999999995
170
  - type: ndcg_at_10
171
+ value: 42.675999999999995
172
  - type: ndcg_at_100
173
+ value: 50.12799999999999
174
  - type: ndcg_at_1000
175
+ value: 52.321
176
  - type: ndcg_at_3
177
+ value: 37.864
178
  - type: ndcg_at_5
179
+ value: 39.701
180
  - type: precision_at_1
181
+ value: 37.608999999999995
182
  - type: precision_at_10
183
+ value: 9.527
184
  - type: precision_at_100
185
+ value: 1.555
186
  - type: precision_at_1000
187
  value: 0.183
188
  - type: precision_at_3
189
+ value: 21.547
190
  - type: precision_at_5
191
+ value: 15.504000000000001
192
  - type: recall_at_1
193
+ value: 24.484
194
  - type: recall_at_10
195
+ value: 52.43299999999999
196
  - type: recall_at_100
197
+ value: 83.446
198
  - type: recall_at_1000
199
+ value: 98.24199999999999
200
  - type: recall_at_3
201
+ value: 37.653
202
  - type: recall_at_5
203
+ value: 43.643
204
  - task:
205
  type: PairClassification
206
  dataset:
 
211
  revision: None
212
  metrics:
213
  - type: cos_sim_accuracy
214
+ value: 77.71497294046902
215
  - type: cos_sim_ap
216
+ value: 86.84542027578229
217
  - type: cos_sim_f1
218
+ value: 79.31987247608926
219
  - type: cos_sim_precision
220
+ value: 72.70601987142022
221
  - type: cos_sim_recall
222
+ value: 87.2574234276362
223
  - type: dot_accuracy
224
+ value: 77.71497294046902
225
  - type: dot_ap
226
+ value: 86.86514752961159
227
  - type: dot_f1
228
+ value: 79.31987247608926
229
  - type: dot_precision
230
+ value: 72.70601987142022
231
  - type: dot_recall
232
+ value: 87.2574234276362
233
  - type: euclidean_accuracy
234
+ value: 77.71497294046902
235
  - type: euclidean_ap
236
+ value: 86.84541456571337
237
  - type: euclidean_f1
238
+ value: 79.31987247608926
239
  - type: euclidean_precision
240
+ value: 72.70601987142022
241
  - type: euclidean_recall
242
+ value: 87.2574234276362
243
  - type: manhattan_accuracy
244
+ value: 77.8111846061335
245
  - type: manhattan_ap
246
+ value: 86.81148050422539
247
  - type: manhattan_f1
248
+ value: 79.41176470588236
249
  - type: manhattan_precision
250
+ value: 72.52173913043478
251
  - type: manhattan_recall
252
+ value: 87.74842179097499
253
  - type: max_accuracy
254
+ value: 77.8111846061335
255
  - type: max_ap
256
+ value: 86.86514752961159
257
  - type: max_f1
258
+ value: 79.41176470588236
259
  - task:
260
  type: Retrieval
261
  dataset:
 
266
  revision: None
267
  metrics:
268
  - type: map_at_1
269
+ value: 68.862
270
  - type: map_at_10
271
+ value: 77.079
272
  - type: map_at_100
273
+ value: 77.428
274
  - type: map_at_1000
275
+ value: 77.432
276
  - type: map_at_3
277
+ value: 75.40400000000001
278
  - type: map_at_5
279
+ value: 76.227
280
  - type: mrr_at_1
281
+ value: 69.02000000000001
282
  - type: mrr_at_10
283
+ value: 77.04299999999999
284
  - type: mrr_at_100
285
+ value: 77.391
286
  - type: mrr_at_1000
287
+ value: 77.395
288
  - type: mrr_at_3
289
+ value: 75.44800000000001
290
  - type: mrr_at_5
291
+ value: 76.23299999999999
292
  - type: ndcg_at_1
293
+ value: 69.02000000000001
294
  - type: ndcg_at_10
295
+ value: 80.789
296
  - type: ndcg_at_100
297
+ value: 82.27499999999999
298
  - type: ndcg_at_1000
299
+ value: 82.381
300
  - type: ndcg_at_3
301
+ value: 77.40599999999999
302
  - type: ndcg_at_5
303
+ value: 78.87100000000001
304
  - type: precision_at_1
305
+ value: 69.02000000000001
306
  - type: precision_at_10
307
+ value: 9.336
308
  - type: precision_at_100
309
+ value: 0.9990000000000001
310
  - type: precision_at_1000
311
  value: 0.101
312
  - type: precision_at_3
313
+ value: 27.889000000000003
314
  - type: precision_at_5
315
+ value: 17.492
316
  - type: recall_at_1
317
+ value: 68.862
318
  - type: recall_at_10
319
+ value: 92.308
320
  - type: recall_at_100
321
+ value: 98.84100000000001
322
  - type: recall_at_1000
323
+ value: 99.684
324
  - type: recall_at_3
325
+ value: 83.087
326
  - type: recall_at_5
327
+ value: 86.617
328
  - task:
329
  type: Retrieval
330
  dataset:
 
335
  revision: None
336
  metrics:
337
  - type: map_at_1
338
+ value: 25.063999999999997
339
  - type: map_at_10
340
+ value: 78.014
341
  - type: map_at_100
342
+ value: 81.021
343
  - type: map_at_1000
344
+ value: 81.059
345
  - type: map_at_3
346
+ value: 53.616
347
  - type: map_at_5
348
+ value: 68.00399999999999
349
  - type: mrr_at_1
350
+ value: 87.8
351
  - type: mrr_at_10
352
+ value: 91.824
353
  - type: mrr_at_100
354
+ value: 91.915
355
  - type: mrr_at_1000
356
+ value: 91.917
357
  - type: mrr_at_3
358
+ value: 91.525
359
  - type: mrr_at_5
360
+ value: 91.752
361
  - type: ndcg_at_1
362
+ value: 87.8
363
  - type: ndcg_at_10
364
+ value: 85.74199999999999
365
  - type: ndcg_at_100
366
+ value: 88.82900000000001
367
  - type: ndcg_at_1000
368
+ value: 89.208
369
  - type: ndcg_at_3
370
+ value: 84.206
371
  - type: ndcg_at_5
372
+ value: 83.421
373
  - type: precision_at_1
374
+ value: 87.8
375
  - type: precision_at_10
376
+ value: 41.325
377
  - type: precision_at_100
378
+ value: 4.8
379
  - type: precision_at_1000
380
  value: 0.48900000000000005
381
  - type: precision_at_3
382
+ value: 75.783
383
  - type: precision_at_5
384
+ value: 64.25999999999999
385
  - type: recall_at_1
386
+ value: 25.063999999999997
387
  - type: recall_at_10
388
+ value: 87.324
389
  - type: recall_at_100
390
+ value: 97.261
391
  - type: recall_at_1000
392
+ value: 99.309
393
  - type: recall_at_3
394
+ value: 56.281000000000006
395
  - type: recall_at_5
396
+ value: 73.467
397
  - task:
398
  type: Retrieval
399
  dataset:
 
404
  revision: None
405
  metrics:
406
  - type: map_at_1
407
+ value: 46.800000000000004
408
  - type: map_at_10
409
+ value: 56.887
410
  - type: map_at_100
411
+ value: 57.556
412
  - type: map_at_1000
413
+ value: 57.582
414
  - type: map_at_3
415
+ value: 54.15
416
  - type: map_at_5
417
+ value: 55.825
418
  - type: mrr_at_1
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+ value: 46.800000000000004
420
  - type: mrr_at_10
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+ value: 56.887
422
  - type: mrr_at_100
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+ value: 57.556
424
  - type: mrr_at_1000
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+ value: 57.582
426
  - type: mrr_at_3
427
+ value: 54.15
428
  - type: mrr_at_5
429
+ value: 55.825
430
  - type: ndcg_at_1
431
+ value: 46.800000000000004
432
  - type: ndcg_at_10
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+ value: 62.061
434
  - type: ndcg_at_100
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+ value: 65.042
436
  - type: ndcg_at_1000
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+ value: 65.658
438
  - type: ndcg_at_3
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+ value: 56.52700000000001
440
  - type: ndcg_at_5
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+ value: 59.518
442
  - type: precision_at_1
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+ value: 46.800000000000004
444
  - type: precision_at_10
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+ value: 7.84
446
  - type: precision_at_100
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+ value: 0.9169999999999999
448
  - type: precision_at_1000
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+ value: 0.096
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  - type: precision_at_3
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+ value: 21.133
452
  - type: precision_at_5
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+ value: 14.12
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  - type: recall_at_1
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+ value: 46.800000000000004
456
  - type: recall_at_10
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+ value: 78.4
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  - type: recall_at_100
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+ value: 91.7
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  - type: recall_at_1000
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+ value: 96.39999999999999
462
  - type: recall_at_3
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+ value: 63.4
464
  - type: recall_at_5
465
+ value: 70.6
466
  - task:
467
  type: Classification
468
  dataset:
 
473
  revision: None
474
  metrics:
475
  - type: accuracy
476
+ value: 48.010773374374764
477
  - type: f1
478
+ value: 35.25314495210735
479
  - task:
480
  type: Classification
481
  dataset:
 
486
  revision: None
487
  metrics:
488
  - type: accuracy
489
+ value: 87.01688555347093
490
  - type: ap
491
+ value: 56.39167630414159
492
  - type: f1
493
+ value: 81.91756262306008
494
  - task:
495
  type: STS
496
  dataset:
 
501
  revision: None
502
  metrics:
503
  - type: cos_sim_pearson
504
+ value: 71.17867432738112
505
  - type: cos_sim_spearman
506
+ value: 77.47954247528372
507
  - type: euclidean_pearson
508
+ value: 76.32408876437825
509
  - type: euclidean_spearman
510
+ value: 77.47954025694959
511
  - type: manhattan_pearson
512
+ value: 76.33345801575938
513
  - type: manhattan_spearman
514
+ value: 77.48901582125997
515
  - task:
516
  type: Reranking
517
  dataset:
 
522
  revision: None
523
  metrics:
524
  - type: map
525
+ value: 27.96333052746654
526
  - type: mrr
527
+ value: 26.92023809523809
528
  - task:
529
  type: Retrieval
530
  dataset:
 
535
  revision: None
536
  metrics:
537
  - type: map_at_1
538
+ value: 66.144
539
  - type: map_at_10
540
+ value: 75.036
541
  - type: map_at_100
542
+ value: 75.36
543
  - type: map_at_1000
544
+ value: 75.371
545
  - type: map_at_3
546
+ value: 73.258
547
  - type: map_at_5
548
+ value: 74.369
549
  - type: mrr_at_1
550
+ value: 68.381
551
  - type: mrr_at_10
552
+ value: 75.633
553
  - type: mrr_at_100
554
+ value: 75.91799999999999
555
  - type: mrr_at_1000
556
+ value: 75.928
557
  - type: mrr_at_3
558
+ value: 74.093
559
  - type: mrr_at_5
560
+ value: 75.036
561
  - type: ndcg_at_1
562
+ value: 68.381
563
  - type: ndcg_at_10
564
+ value: 78.661
565
  - type: ndcg_at_100
566
+ value: 80.15
567
  - type: ndcg_at_1000
568
+ value: 80.456
569
  - type: ndcg_at_3
570
+ value: 75.295
571
  - type: ndcg_at_5
572
+ value: 77.14999999999999
573
  - type: precision_at_1
574
+ value: 68.381
575
  - type: precision_at_10
576
+ value: 9.481
577
  - type: precision_at_100
578
+ value: 1.023
579
  - type: precision_at_1000
580
  value: 0.105
581
  - type: precision_at_3
582
+ value: 28.309
583
  - type: precision_at_5
584
+ value: 17.974
585
  - type: recall_at_1
586
+ value: 66.144
587
  - type: recall_at_10
588
+ value: 89.24499999999999
589
  - type: recall_at_100
590
+ value: 96.032
591
  - type: recall_at_1000
592
+ value: 98.437
593
  - type: recall_at_3
594
+ value: 80.327
595
  - type: recall_at_5
596
+ value: 84.733
597
  - task:
598
  type: Classification
599
  dataset:
 
604
  revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
605
  metrics:
606
  - type: accuracy
607
+ value: 68.26832548755884
608
  - type: f1
609
+ value: 65.97422207086723
610
  - task:
611
  type: Classification
612
  dataset:
 
617
  revision: 7d571f92784cd94a019292a1f45445077d0ef634
618
  metrics:
619
  - type: accuracy
620
+ value: 73.13046402151984
621
  - type: f1
622
+ value: 72.69199129694121
623
  - task:
624
  type: Retrieval
625
  dataset:
 
630
  revision: None
631
  metrics:
632
  - type: map_at_1
633
+ value: 50.4
634
  - type: map_at_10
635
+ value: 56.645
636
  - type: map_at_100
637
+ value: 57.160999999999994
638
  - type: map_at_1000
639
+ value: 57.218
640
  - type: map_at_3
641
+ value: 55.383
642
  - type: map_at_5
643
+ value: 56.08800000000001
644
  - type: mrr_at_1
645
+ value: 50.6
646
  - type: mrr_at_10
647
+ value: 56.745999999999995
648
  - type: mrr_at_100
649
+ value: 57.262
650
  - type: mrr_at_1000
651
+ value: 57.318999999999996
652
  - type: mrr_at_3
653
+ value: 55.483000000000004
654
  - type: mrr_at_5
655
+ value: 56.188
656
  - type: ndcg_at_1
657
+ value: 50.4
658
  - type: ndcg_at_10
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+ value: 59.534
660
  - type: ndcg_at_100
661
+ value: 62.400999999999996
662
  - type: ndcg_at_1000
663
+ value: 64.01299999999999
664
  - type: ndcg_at_3
665
+ value: 56.887
666
  - type: ndcg_at_5
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+ value: 58.160000000000004
668
  - type: precision_at_1
669
+ value: 50.4
670
  - type: precision_at_10
671
+ value: 6.859999999999999
672
  - type: precision_at_100
673
+ value: 0.828
674
  - type: precision_at_1000
675
+ value: 0.096
676
  - type: precision_at_3
677
+ value: 20.4
678
  - type: precision_at_5
679
+ value: 12.86
680
  - type: recall_at_1
681
+ value: 50.4
682
  - type: recall_at_10
683
+ value: 68.60000000000001
684
  - type: recall_at_100
685
+ value: 82.8
686
  - type: recall_at_1000
687
+ value: 95.7
688
  - type: recall_at_3
689
+ value: 61.199999999999996
690
  - type: recall_at_5
691
+ value: 64.3
692
  - task:
693
  type: Classification
694
  dataset:
 
699
  revision: None
700
  metrics:
701
  - type: accuracy
702
+ value: 73.39666666666666
703
  - type: f1
704
+ value: 72.86349039489504
705
  - task:
706
  type: PairClassification
707
  dataset:
 
712
  revision: None
713
  metrics:
714
  - type: cos_sim_accuracy
715
+ value: 73.36220898754738
716
  - type: cos_sim_ap
717
+ value: 78.50300066088354
718
  - type: cos_sim_f1
719
+ value: 75.39370078740157
720
  - type: cos_sim_precision
721
+ value: 70.59907834101382
722
  - type: cos_sim_recall
723
+ value: 80.8870116156283
724
  - type: dot_accuracy
725
+ value: 73.36220898754738
726
  - type: dot_ap
727
+ value: 78.50300066088354
728
  - type: dot_f1
729
+ value: 75.39370078740157
730
  - type: dot_precision
731
+ value: 70.59907834101382
732
  - type: dot_recall
733
+ value: 80.8870116156283
734
  - type: euclidean_accuracy
735
+ value: 73.36220898754738
736
  - type: euclidean_ap
737
+ value: 78.50300066088354
738
  - type: euclidean_f1
739
+ value: 75.39370078740157
740
  - type: euclidean_precision
741
+ value: 70.59907834101382
742
  - type: euclidean_recall
743
+ value: 80.8870116156283
744
  - type: manhattan_accuracy
745
+ value: 73.09149972929075
746
  - type: manhattan_ap
747
+ value: 78.41160715817406
748
  - type: manhattan_f1
749
+ value: 75.3623188405797
750
  - type: manhattan_precision
751
+ value: 69.45681211041853
752
  - type: manhattan_recall
753
+ value: 82.36536430834214
754
  - type: max_accuracy
755
+ value: 73.36220898754738
756
  - type: max_ap
757
+ value: 78.50300066088354
758
  - type: max_f1
759
+ value: 75.39370078740157
760
  - task:
761
  type: Classification
762
  dataset:
 
767
  revision: None
768
  metrics:
769
  - type: accuracy
770
+ value: 91.82000000000001
771
  - type: ap
772
+ value: 89.3671278896903
773
  - type: f1
774
+ value: 91.8021970144045
775
  - task:
776
  type: STS
777
  dataset:
 
782
  revision: None
783
  metrics:
784
  - type: cos_sim_pearson
785
+ value: 30.07022294131062
786
  - type: cos_sim_spearman
787
+ value: 36.21542804954441
788
  - type: euclidean_pearson
789
+ value: 36.37841945307606
790
  - type: euclidean_spearman
791
+ value: 36.215513214835546
792
  - type: manhattan_pearson
793
+ value: 36.31755715017088
794
  - type: manhattan_spearman
795
+ value: 36.16848256918425
796
  - task:
797
  type: STS
798
  dataset:
 
803
  revision: None
804
  metrics:
805
  - type: cos_sim_pearson
806
+ value: 36.779755871073505
807
  - type: cos_sim_spearman
808
+ value: 38.736220679196606
809
  - type: euclidean_pearson
810
+ value: 37.13356686891227
811
  - type: euclidean_spearman
812
+ value: 38.73619198602118
813
  - type: manhattan_pearson
814
+ value: 37.175466658530816
815
  - type: manhattan_spearman
816
+ value: 38.74523158724344
817
  - task:
818
  type: STS
819
  dataset:
 
824
  revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
825
  metrics:
826
  - type: cos_sim_pearson
827
+ value: 65.9737863254904
828
  - type: cos_sim_spearman
829
+ value: 68.88293545840186
830
  - type: euclidean_pearson
831
+ value: 67.23730973929247
832
  - type: euclidean_spearman
833
+ value: 68.88293545840186
834
  - type: manhattan_pearson
835
+ value: 67.30647960940956
836
  - type: manhattan_spearman
837
+ value: 68.90553460682702
838
  - task:
839
  type: STS
840
  dataset:
 
845
  revision: None
846
  metrics:
847
  - type: cos_sim_pearson
848
+ value: 78.99371432933002
849
  - type: cos_sim_spearman
850
+ value: 79.36496709214312
851
  - type: euclidean_pearson
852
+ value: 78.77721120706431
853
  - type: euclidean_spearman
854
+ value: 79.36500761622595
855
  - type: manhattan_pearson
856
+ value: 78.82503201285202
857
  - type: manhattan_spearman
858
+ value: 79.43915548337401
859
  - task:
860
  type: Reranking
861
  dataset:
 
866
  revision: None
867
  metrics:
868
  - type: map
869
+ value: 66.38418982516941
870
  - type: mrr
871
+ value: 76.09996131153883
872
  - task:
873
  type: Retrieval
874
  dataset:
 
879
  revision: None
880
  metrics:
881
  - type: map_at_1
882
+ value: 27.426000000000002
883
  - type: map_at_10
884
+ value: 77.209
885
  - type: map_at_100
886
+ value: 80.838
887
  - type: map_at_1000
888
+ value: 80.903
889
  - type: map_at_3
890
+ value: 54.196
891
  - type: map_at_5
892
+ value: 66.664
893
  - type: mrr_at_1
894
+ value: 90.049
895
  - type: mrr_at_10
896
+ value: 92.482
897
  - type: mrr_at_100
898
+ value: 92.568
899
  - type: mrr_at_1000
900
+ value: 92.572
901
  - type: mrr_at_3
902
+ value: 92.072
903
  - type: mrr_at_5
904
+ value: 92.33
905
  - type: ndcg_at_1
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+ value: 90.049
907
  - type: ndcg_at_10
908
+ value: 84.69200000000001
909
  - type: ndcg_at_100
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+ value: 88.25699999999999
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  - type: ndcg_at_1000
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+ value: 88.896
913
  - type: ndcg_at_3
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+ value: 86.09700000000001
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  - type: ndcg_at_5
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+ value: 84.68599999999999
917
  - type: precision_at_1
918
+ value: 90.049
919
  - type: precision_at_10
920
+ value: 42.142
921
  - type: precision_at_100
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+ value: 5.017
923
  - type: precision_at_1000
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  value: 0.516
925
  - type: precision_at_3
926
+ value: 75.358
927
  - type: precision_at_5
928
+ value: 63.173
929
  - type: recall_at_1
930
+ value: 27.426000000000002
931
  - type: recall_at_10
932
+ value: 83.59400000000001
933
  - type: recall_at_100
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+ value: 95.21
935
  - type: recall_at_1000
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+ value: 98.503
937
  - type: recall_at_3
938
+ value: 55.849000000000004
939
  - type: recall_at_5
940
+ value: 69.986
941
  - task:
942
  type: Classification
943
  dataset:
 
948
  revision: None
949
  metrics:
950
  - type: accuracy
951
+ value: 51.925999999999995
952
  - type: f1
953
+ value: 50.16867723626971
954
  - task:
955
  type: Clustering
956
  dataset:
 
961
  revision: None
962
  metrics:
963
  - type: v_measure
964
+ value: 60.738901671970005
965
  - task:
966
  type: Clustering
967
  dataset:
 
972
  revision: None
973
  metrics:
974
  - type: v_measure
975
+ value: 57.08563183138733
976
  - task:
977
  type: Retrieval
978
  dataset:
 
983
  revision: None
984
  metrics:
985
  - type: map_at_1
986
+ value: 52.0
987
  - type: map_at_10
988
+ value: 62.956
989
  - type: map_at_100
990
+ value: 63.491
991
  - type: map_at_1000
992
+ value: 63.50599999999999
993
  - type: map_at_3
994
+ value: 60.733000000000004
995
  - type: map_at_5
996
+ value: 62.217999999999996
997
  - type: mrr_at_1
998
+ value: 52.0
999
  - type: mrr_at_10
1000
+ value: 62.956
1001
  - type: mrr_at_100
1002
+ value: 63.491
1003
  - type: mrr_at_1000
1004
+ value: 63.50599999999999
1005
  - type: mrr_at_3
1006
+ value: 60.733000000000004
1007
  - type: mrr_at_5
1008
+ value: 62.217999999999996
1009
  - type: ndcg_at_1
1010
+ value: 52.0
1011
  - type: ndcg_at_10
1012
+ value: 67.956
1013
  - type: ndcg_at_100
1014
+ value: 70.536
1015
  - type: ndcg_at_1000
1016
+ value: 70.908
1017
  - type: ndcg_at_3
1018
+ value: 63.456999999999994
1019
  - type: ndcg_at_5
1020
+ value: 66.155
1021
  - type: precision_at_1
1022
+ value: 52.0
1023
  - type: precision_at_10
1024
+ value: 8.35
1025
  - type: precision_at_100
1026
+ value: 0.955
1027
  - type: precision_at_1000
1028
+ value: 0.098
1029
  - type: precision_at_3
1030
+ value: 23.767
1031
  - type: precision_at_5
1032
+ value: 15.58
1033
  - type: recall_at_1
1034
+ value: 52.0
1035
  - type: recall_at_10
1036
+ value: 83.5
1037
  - type: recall_at_100
1038
+ value: 95.5
1039
  - type: recall_at_1000
1040
+ value: 98.4
1041
  - type: recall_at_3
1042
+ value: 71.3
1043
  - type: recall_at_5
1044
+ value: 77.9
1045
  - task:
1046
  type: Classification
1047
  dataset:
 
1052
  revision: None
1053
  metrics:
1054
  - type: accuracy
1055
+ value: 87.10000000000001
1056
  - type: ap
1057
+ value: 70.81766065881429
1058
  - type: f1
1059
+ value: 85.5323306120456
1060
  ---
1061
 
1062
  a try for emebdding model
config.json CHANGED
@@ -1,35 +1,35 @@
1
  {
2
- "_name_or_path": "./model/tao-8k",
3
- "architectures": [
4
- "BertModel"
5
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