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Add MTEB results

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Add MTEB results for most tasks, excluding a few retrieval tasks because of limited resources. These might be added in future.

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@@ -4,6 +4,1705 @@ tags:
4
  - sentence-transformers
5
  - feature-extraction
6
  - sentence-similarity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7
  ---
8
 
9
  # clip-ViT-B-32
 
4
  - sentence-transformers
5
  - feature-extraction
6
  - sentence-similarity
7
+ - mteb
8
+ model-index:
9
+ - name: clip-ViT-B-32
10
+ results:
11
+ - task:
12
+ type: Classification
13
+ dataset:
14
+ type: mteb/amazon_counterfactual
15
+ name: MTEB AmazonCounterfactualClassification (en)
16
+ config: en
17
+ split: test
18
+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
19
+ metrics:
20
+ - type: accuracy
21
+ value: 57.999999999999986
22
+ - type: ap
23
+ value: 23.966099106216358
24
+ - type: f1
25
+ value: 52.8203944454417
26
+ - task:
27
+ type: Classification
28
+ dataset:
29
+ type: mteb/amazon_polarity
30
+ name: MTEB AmazonPolarityClassification
31
+ config: default
32
+ split: test
33
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
34
+ metrics:
35
+ - type: accuracy
36
+ value: 62.366
37
+ - type: ap
38
+ value: 57.98090324593318
39
+ - type: f1
40
+ value: 61.62762218315074
41
+ - task:
42
+ type: Classification
43
+ dataset:
44
+ type: mteb/amazon_reviews_multi
45
+ name: MTEB AmazonReviewsClassification (en)
46
+ config: en
47
+ split: test
48
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
49
+ metrics:
50
+ - type: accuracy
51
+ value: 28.584
52
+ - type: f1
53
+ value: 28.463306116150783
54
+ - task:
55
+ type: Retrieval
56
+ dataset:
57
+ type: arguana
58
+ name: MTEB ArguAna
59
+ config: default
60
+ split: test
61
+ revision: None
62
+ metrics:
63
+ - type: map_at_1
64
+ value: 6.259
65
+ - type: map_at_10
66
+ value: 11.542
67
+ - type: map_at_100
68
+ value: 12.859000000000002
69
+ - type: map_at_1000
70
+ value: 12.966
71
+ - type: map_at_3
72
+ value: 9.128
73
+ - type: map_at_5
74
+ value: 10.262
75
+ - type: mrr_at_1
76
+ value: 6.259
77
+ - type: mrr_at_10
78
+ value: 11.536
79
+ - type: mrr_at_100
80
+ value: 12.859000000000002
81
+ - type: mrr_at_1000
82
+ value: 12.967
83
+ - type: mrr_at_3
84
+ value: 9.128
85
+ - type: mrr_at_5
86
+ value: 10.262
87
+ - type: ndcg_at_1
88
+ value: 6.259
89
+ - type: ndcg_at_10
90
+ value: 15.35
91
+ - type: ndcg_at_100
92
+ value: 22.107
93
+ - type: ndcg_at_1000
94
+ value: 25.355
95
+ - type: ndcg_at_3
96
+ value: 10.172
97
+ - type: ndcg_at_5
98
+ value: 12.22
99
+ - type: precision_at_1
100
+ value: 6.259
101
+ - type: precision_at_10
102
+ value: 2.795
103
+ - type: precision_at_100
104
+ value: 0.603
105
+ - type: precision_at_1000
106
+ value: 0.087
107
+ - type: precision_at_3
108
+ value: 4.41
109
+ - type: precision_at_5
110
+ value: 3.642
111
+ - type: recall_at_1
112
+ value: 6.259
113
+ - type: recall_at_10
114
+ value: 27.951999999999998
115
+ - type: recall_at_100
116
+ value: 60.313
117
+ - type: recall_at_1000
118
+ value: 86.771
119
+ - type: recall_at_3
120
+ value: 13.229
121
+ - type: recall_at_5
122
+ value: 18.208
123
+ - task:
124
+ type: Clustering
125
+ dataset:
126
+ type: mteb/arxiv-clustering-p2p
127
+ name: MTEB ArxivClusteringP2P
128
+ config: default
129
+ split: test
130
+ revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
131
+ metrics:
132
+ - type: v_measure
133
+ value: 30.95753257205936
134
+ - task:
135
+ type: Clustering
136
+ dataset:
137
+ type: mteb/arxiv-clustering-s2s
138
+ name: MTEB ArxivClusteringS2S
139
+ config: default
140
+ split: test
141
+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
142
+ metrics:
143
+ - type: v_measure
144
+ value: 26.586511396557583
145
+ - task:
146
+ type: Reranking
147
+ dataset:
148
+ type: mteb/askubuntudupquestions-reranking
149
+ name: MTEB AskUbuntuDupQuestions
150
+ config: default
151
+ split: test
152
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
153
+ metrics:
154
+ - type: map
155
+ value: 51.090393666506415
156
+ - type: mrr
157
+ value: 65.19412566503979
158
+ - task:
159
+ type: STS
160
+ dataset:
161
+ type: mteb/biosses-sts
162
+ name: MTEB BIOSSES
163
+ config: default
164
+ split: test
165
+ revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
166
+ metrics:
167
+ - type: cos_sim_pearson
168
+ value: 69.9163188743249
169
+ - type: cos_sim_spearman
170
+ value: 64.1345938803495
171
+ - type: euclidean_pearson
172
+ value: 67.36703723549599
173
+ - type: euclidean_spearman
174
+ value: 63.067702100617005
175
+ - type: manhattan_pearson
176
+ value: 71.6901307580259
177
+ - type: manhattan_spearman
178
+ value: 67.04128661733944
179
+ - task:
180
+ type: Classification
181
+ dataset:
182
+ type: mteb/banking77
183
+ name: MTEB Banking77Classification
184
+ config: default
185
+ split: test
186
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
187
+ metrics:
188
+ - type: accuracy
189
+ value: 73.22402597402598
190
+ - type: f1
191
+ value: 73.12739303105114
192
+ - task:
193
+ type: Clustering
194
+ dataset:
195
+ type: mteb/biorxiv-clustering-p2p
196
+ name: MTEB BiorxivClusteringP2P
197
+ config: default
198
+ split: test
199
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
200
+ metrics:
201
+ - type: v_measure
202
+ value: 28.97385566120484
203
+ - task:
204
+ type: Clustering
205
+ dataset:
206
+ type: mteb/biorxiv-clustering-s2s
207
+ name: MTEB BiorxivClusteringS2S
208
+ config: default
209
+ split: test
210
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
211
+ metrics:
212
+ - type: v_measure
213
+ value: 27.08579813861177
214
+ - task:
215
+ type: Retrieval
216
+ dataset:
217
+ type: BeIR/cqadupstack
218
+ name: MTEB CQADupstackAndroidRetrieval
219
+ config: default
220
+ split: test
221
+ revision: None
222
+ metrics:
223
+ - type: map_at_1
224
+ value: 7.106999999999999
225
+ - type: map_at_10
226
+ value: 11.797
227
+ - type: map_at_100
228
+ value: 12.6
229
+ - type: map_at_1000
230
+ value: 12.711
231
+ - type: map_at_3
232
+ value: 10.369
233
+ - type: map_at_5
234
+ value: 10.881
235
+ - type: mrr_at_1
236
+ value: 9.299
237
+ - type: mrr_at_10
238
+ value: 15.076
239
+ - type: mrr_at_100
240
+ value: 15.842
241
+ - type: mrr_at_1000
242
+ value: 15.928
243
+ - type: mrr_at_3
244
+ value: 13.4
245
+ - type: mrr_at_5
246
+ value: 14.044
247
+ - type: ndcg_at_1
248
+ value: 9.299
249
+ - type: ndcg_at_10
250
+ value: 15.21
251
+ - type: ndcg_at_100
252
+ value: 19.374
253
+ - type: ndcg_at_1000
254
+ value: 22.527
255
+ - type: ndcg_at_3
256
+ value: 12.383
257
+ - type: ndcg_at_5
258
+ value: 13.096
259
+ - type: precision_at_1
260
+ value: 9.299
261
+ - type: precision_at_10
262
+ value: 3.1620000000000004
263
+ - type: precision_at_100
264
+ value: 0.662
265
+ - type: precision_at_1000
266
+ value: 0.11800000000000001
267
+ - type: precision_at_3
268
+ value: 6.3420000000000005
269
+ - type: precision_at_5
270
+ value: 4.492
271
+ - type: recall_at_1
272
+ value: 7.106999999999999
273
+ - type: recall_at_10
274
+ value: 22.544
275
+ - type: recall_at_100
276
+ value: 41.002
277
+ - type: recall_at_1000
278
+ value: 63.67699999999999
279
+ - type: recall_at_3
280
+ value: 14.316999999999998
281
+ - type: recall_at_5
282
+ value: 16.367
283
+ - task:
284
+ type: Retrieval
285
+ dataset:
286
+ type: BeIR/cqadupstack
287
+ name: MTEB CQADupstackEnglishRetrieval
288
+ config: default
289
+ split: test
290
+ revision: None
291
+ metrics:
292
+ - type: map_at_1
293
+ value: 6.632000000000001
294
+ - type: map_at_10
295
+ value: 9.067
296
+ - type: map_at_100
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+ value: 9.487
298
+ - type: map_at_1000
299
+ value: 9.563
300
+ - type: map_at_3
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+ value: 8.344999999999999
302
+ - type: map_at_5
303
+ value: 8.742999999999999
304
+ - type: mrr_at_1
305
+ value: 8.599
306
+ - type: mrr_at_10
307
+ value: 11.332
308
+ - type: mrr_at_100
309
+ value: 11.77
310
+ - type: mrr_at_1000
311
+ value: 11.843
312
+ - type: mrr_at_3
313
+ value: 10.478
314
+ - type: mrr_at_5
315
+ value: 10.959000000000001
316
+ - type: ndcg_at_1
317
+ value: 8.599
318
+ - type: ndcg_at_10
319
+ value: 10.843
320
+ - type: ndcg_at_100
321
+ value: 13.023000000000001
322
+ - type: ndcg_at_1000
323
+ value: 15.409
324
+ - type: ndcg_at_3
325
+ value: 9.673
326
+ - type: ndcg_at_5
327
+ value: 10.188
328
+ - type: precision_at_1
329
+ value: 8.599
330
+ - type: precision_at_10
331
+ value: 2.038
332
+ - type: precision_at_100
333
+ value: 0.383
334
+ - type: precision_at_1000
335
+ value: 0.074
336
+ - type: precision_at_3
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+ value: 4.756
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+ - type: precision_at_5
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+ value: 3.3890000000000002
340
+ - type: recall_at_1
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+ value: 6.632000000000001
342
+ - type: recall_at_10
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+ value: 13.952
344
+ - type: recall_at_100
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+ value: 23.966
346
+ - type: recall_at_1000
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+ value: 41.411
348
+ - type: recall_at_3
349
+ value: 10.224
350
+ - type: recall_at_5
351
+ value: 11.799
352
+ - task:
353
+ type: Retrieval
354
+ dataset:
355
+ type: BeIR/cqadupstack
356
+ name: MTEB CQADupstackGamingRetrieval
357
+ config: default
358
+ split: test
359
+ revision: None
360
+ metrics:
361
+ - type: map_at_1
362
+ value: 11.153
363
+ - type: map_at_10
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+ value: 15.751000000000001
365
+ - type: map_at_100
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+ value: 16.464000000000002
367
+ - type: map_at_1000
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+ - type: map_at_3
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+ value: 14.552000000000001
371
+ - type: map_at_5
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+ value: 15.136
373
+ - type: mrr_at_1
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+ value: 13.041
375
+ - type: mrr_at_10
376
+ value: 17.777
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+ - type: mrr_at_100
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+ value: 18.427
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+ - type: mrr_at_1000
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+ value: 18.504
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+ - type: mrr_at_3
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+ value: 16.479
383
+ - type: mrr_at_5
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+ value: 17.175
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+ - type: ndcg_at_1
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+ value: 13.041
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+ - type: ndcg_at_10
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+ value: 18.581
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+ - type: ndcg_at_100
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+ value: 22.174
391
+ - type: ndcg_at_1000
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+ value: 24.795
393
+ - type: ndcg_at_3
394
+ value: 16.185
395
+ - type: ndcg_at_5
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+ value: 17.183
397
+ - type: precision_at_1
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+ value: 13.041
399
+ - type: precision_at_10
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+ value: 3.2230000000000003
401
+ - type: precision_at_100
402
+ value: 0.557
403
+ - type: precision_at_1000
404
+ value: 0.086
405
+ - type: precision_at_3
406
+ value: 7.544
407
+ - type: precision_at_5
408
+ value: 5.279
409
+ - type: recall_at_1
410
+ value: 11.153
411
+ - type: recall_at_10
412
+ value: 25.052999999999997
413
+ - type: recall_at_100
414
+ value: 41.521
415
+ - type: recall_at_1000
416
+ value: 61.138000000000005
417
+ - type: recall_at_3
418
+ value: 18.673000000000002
419
+ - type: recall_at_5
420
+ value: 20.964
421
+ - task:
422
+ type: Retrieval
423
+ dataset:
424
+ type: BeIR/cqadupstack
425
+ name: MTEB CQADupstackGisRetrieval
426
+ config: default
427
+ split: test
428
+ revision: None
429
+ metrics:
430
+ - type: map_at_1
431
+ value: 5.303
432
+ - type: map_at_10
433
+ value: 7.649
434
+ - type: map_at_100
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+ value: 7.983
436
+ - type: map_at_1000
437
+ value: 8.067
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+ - type: map_at_3
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+ value: 6.938
440
+ - type: map_at_5
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+ value: 7.259
442
+ - type: mrr_at_1
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+ value: 5.763
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+ - type: mrr_at_10
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+ value: 8.277
446
+ - type: mrr_at_100
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+ value: 8.665000000000001
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+ - type: mrr_at_1000
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+ value: 8.747
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+ - type: mrr_at_3
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+ value: 7.457999999999999
452
+ - type: mrr_at_5
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+ value: 7.808
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+ - type: ndcg_at_1
455
+ value: 5.763
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+ - type: ndcg_at_10
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+ value: 9.1
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+ - type: ndcg_at_100
459
+ value: 11.253
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+ - type: ndcg_at_1000
461
+ value: 13.847999999999999
462
+ - type: ndcg_at_3
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+ value: 7.521999999999999
464
+ - type: ndcg_at_5
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+ value: 8.094
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+ - type: precision_at_1
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+ value: 5.763
468
+ - type: precision_at_10
469
+ value: 1.514
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+ - type: precision_at_100
471
+ value: 0.28700000000000003
472
+ - type: precision_at_1000
473
+ value: 0.054
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+ - type: precision_at_3
475
+ value: 3.277
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+ - type: precision_at_5
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+ value: 2.282
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+ - type: recall_at_1
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+ value: 5.303
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+ - type: recall_at_10
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+ value: 13.126
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+ - type: recall_at_100
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+ value: 23.855
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+ - type: recall_at_1000
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+ value: 44.417
486
+ - type: recall_at_3
487
+ value: 8.556
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+ - type: recall_at_5
489
+ value: 10.006
490
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491
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493
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498
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499
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560
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561
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562
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563
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567
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568
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631
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632
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700
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771
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775
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+ dataset:
1654
+ type: mteb/twitterurlcorpus-pairclassification
1655
+ name: MTEB TwitterURLCorpus
1656
+ config: default
1657
+ split: test
1658
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
1659
+ metrics:
1660
+ - type: cos_sim_accuracy
1661
+ value: 84.02607986960065
1662
+ - type: cos_sim_ap
1663
+ value: 74.07757228336027
1664
+ - type: cos_sim_f1
1665
+ value: 66.0694778239021
1666
+ - type: cos_sim_precision
1667
+ value: 62.67790520934089
1668
+ - type: cos_sim_recall
1669
+ value: 69.84909146904835
1670
+ - type: dot_accuracy
1671
+ value: 74.79722125198897
1672
+ - type: dot_ap
1673
+ value: 25.478024888904727
1674
+ - type: dot_f1
1675
+ value: 40.76642277589147
1676
+ - type: dot_precision
1677
+ value: 25.705095989546688
1678
+ - type: dot_recall
1679
+ value: 98.45241761626117
1680
+ - type: euclidean_accuracy
1681
+ value: 85.51053673303062
1682
+ - type: euclidean_ap
1683
+ value: 78.24178926488659
1684
+ - type: euclidean_f1
1685
+ value: 70.50944224857267
1686
+ - type: euclidean_precision
1687
+ value: 67.19447544642857
1688
+ - type: euclidean_recall
1689
+ value: 74.16846319679703
1690
+ - type: manhattan_accuracy
1691
+ value: 85.72398804672643
1692
+ - type: manhattan_ap
1693
+ value: 78.90411073933831
1694
+ - type: manhattan_f1
1695
+ value: 70.90586145648314
1696
+ - type: manhattan_precision
1697
+ value: 65.8224508640021
1698
+ - type: manhattan_recall
1699
+ value: 76.84016014782877
1700
+ - type: max_accuracy
1701
+ value: 85.72398804672643
1702
+ - type: max_ap
1703
+ value: 78.90411073933831
1704
+ - type: max_f1
1705
+ value: 70.90586145648314
1706
  ---
1707
 
1708
  # clip-ViT-B-32