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@@ -5,7 +5,726 @@ tags:
5
  - sentence-transformers
6
  - feature-extraction
7
  - sentence-similarity
8
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  ---
10
 
11
  # {MODEL_NAME}
 
5
  - sentence-transformers
6
  - feature-extraction
7
  - sentence-similarity
8
+ - mteb
9
+ model-index:
10
+ - name: bge-m3-custom-fr
11
+ results:
12
+ - task:
13
+ type: Clustering
14
+ dataset:
15
+ type: lyon-nlp/alloprof
16
+ name: MTEB AlloProfClusteringP2P
17
+ config: default
18
+ split: test
19
+ revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
20
+ metrics:
21
+ - type: v_measure
22
+ value: 56.727459716713
23
+ - task:
24
+ type: Clustering
25
+ dataset:
26
+ type: lyon-nlp/alloprof
27
+ name: MTEB AlloProfClusteringS2S
28
+ config: default
29
+ split: test
30
+ revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
31
+ metrics:
32
+ - type: v_measure
33
+ value: 38.19920006179227
34
+ - task:
35
+ type: Reranking
36
+ dataset:
37
+ type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
38
+ name: MTEB AlloprofReranking
39
+ config: default
40
+ split: test
41
+ revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
42
+ metrics:
43
+ - type: map
44
+ value: 65.17465797499942
45
+ - type: mrr
46
+ value: 66.51400197384653
47
+ - task:
48
+ type: Retrieval
49
+ dataset:
50
+ type: lyon-nlp/alloprof
51
+ name: MTEB AlloprofRetrieval
52
+ config: default
53
+ split: test
54
+ revision: 2df7bee4080bedf2e97de3da6bd5c7bc9fc9c4d2
55
+ metrics:
56
+ - type: map_at_1
57
+ value: 29.836000000000002
58
+ - type: map_at_10
59
+ value: 39.916000000000004
60
+ - type: map_at_100
61
+ value: 40.816
62
+ - type: map_at_1000
63
+ value: 40.877
64
+ - type: map_at_3
65
+ value: 37.294
66
+ - type: map_at_5
67
+ value: 38.838
68
+ - type: mrr_at_1
69
+ value: 29.836000000000002
70
+ - type: mrr_at_10
71
+ value: 39.916000000000004
72
+ - type: mrr_at_100
73
+ value: 40.816
74
+ - type: mrr_at_1000
75
+ value: 40.877
76
+ - type: mrr_at_3
77
+ value: 37.294
78
+ - type: mrr_at_5
79
+ value: 38.838
80
+ - type: ndcg_at_1
81
+ value: 29.836000000000002
82
+ - type: ndcg_at_10
83
+ value: 45.097
84
+ - type: ndcg_at_100
85
+ value: 49.683
86
+ - type: ndcg_at_1000
87
+ value: 51.429
88
+ - type: ndcg_at_3
89
+ value: 39.717
90
+ - type: ndcg_at_5
91
+ value: 42.501
92
+ - type: precision_at_1
93
+ value: 29.836000000000002
94
+ - type: precision_at_10
95
+ value: 6.149
96
+ - type: precision_at_100
97
+ value: 0.8340000000000001
98
+ - type: precision_at_1000
99
+ value: 0.097
100
+ - type: precision_at_3
101
+ value: 15.576
102
+ - type: precision_at_5
103
+ value: 10.698
104
+ - type: recall_at_1
105
+ value: 29.836000000000002
106
+ - type: recall_at_10
107
+ value: 61.485
108
+ - type: recall_at_100
109
+ value: 83.428
110
+ - type: recall_at_1000
111
+ value: 97.461
112
+ - type: recall_at_3
113
+ value: 46.727000000000004
114
+ - type: recall_at_5
115
+ value: 53.489
116
+ - task:
117
+ type: Classification
118
+ dataset:
119
+ type: mteb/amazon_reviews_multi
120
+ name: MTEB AmazonReviewsClassification (fr)
121
+ config: fr
122
+ split: test
123
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
124
+ metrics:
125
+ - type: accuracy
126
+ value: 42.332
127
+ - type: f1
128
+ value: 40.801800929404344
129
+ - task:
130
+ type: Retrieval
131
+ dataset:
132
+ type: maastrichtlawtech/bsard
133
+ name: MTEB BSARDRetrieval
134
+ config: default
135
+ split: test
136
+ revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
137
+ metrics:
138
+ - type: map_at_1
139
+ value: 0.0
140
+ - type: map_at_10
141
+ value: 0.0
142
+ - type: map_at_100
143
+ value: 0.011000000000000001
144
+ - type: map_at_1000
145
+ value: 0.018000000000000002
146
+ - type: map_at_3
147
+ value: 0.0
148
+ - type: map_at_5
149
+ value: 0.0
150
+ - type: mrr_at_1
151
+ value: 0.0
152
+ - type: mrr_at_10
153
+ value: 0.0
154
+ - type: mrr_at_100
155
+ value: 0.011000000000000001
156
+ - type: mrr_at_1000
157
+ value: 0.018000000000000002
158
+ - type: mrr_at_3
159
+ value: 0.0
160
+ - type: mrr_at_5
161
+ value: 0.0
162
+ - type: ndcg_at_1
163
+ value: 0.0
164
+ - type: ndcg_at_10
165
+ value: 0.0
166
+ - type: ndcg_at_100
167
+ value: 0.13999999999999999
168
+ - type: ndcg_at_1000
169
+ value: 0.457
170
+ - type: ndcg_at_3
171
+ value: 0.0
172
+ - type: ndcg_at_5
173
+ value: 0.0
174
+ - type: precision_at_1
175
+ value: 0.0
176
+ - type: precision_at_10
177
+ value: 0.0
178
+ - type: precision_at_100
179
+ value: 0.009000000000000001
180
+ - type: precision_at_1000
181
+ value: 0.004
182
+ - type: precision_at_3
183
+ value: 0.0
184
+ - type: precision_at_5
185
+ value: 0.0
186
+ - type: recall_at_1
187
+ value: 0.0
188
+ - type: recall_at_10
189
+ value: 0.0
190
+ - type: recall_at_100
191
+ value: 0.901
192
+ - type: recall_at_1000
193
+ value: 3.604
194
+ - type: recall_at_3
195
+ value: 0.0
196
+ - type: recall_at_5
197
+ value: 0.0
198
+ - task:
199
+ type: Clustering
200
+ dataset:
201
+ type: lyon-nlp/clustering-hal-s2s
202
+ name: MTEB HALClusteringS2S
203
+ config: default
204
+ split: test
205
+ revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
206
+ metrics:
207
+ - type: v_measure
208
+ value: 24.1294565929144
209
+ - task:
210
+ type: Clustering
211
+ dataset:
212
+ type: mlsum
213
+ name: MTEB MLSUMClusteringP2P
214
+ config: default
215
+ split: test
216
+ revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
217
+ metrics:
218
+ - type: v_measure
219
+ value: 42.12040762356958
220
+ - task:
221
+ type: Clustering
222
+ dataset:
223
+ type: mlsum
224
+ name: MTEB MLSUMClusteringS2S
225
+ config: default
226
+ split: test
227
+ revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
228
+ metrics:
229
+ - type: v_measure
230
+ value: 36.69102548662494
231
+ - task:
232
+ type: Classification
233
+ dataset:
234
+ type: mteb/mtop_domain
235
+ name: MTEB MTOPDomainClassification (fr)
236
+ config: fr
237
+ split: test
238
+ revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
239
+ metrics:
240
+ - type: accuracy
241
+ value: 90.3946132164109
242
+ - type: f1
243
+ value: 90.15608090764273
244
+ - task:
245
+ type: Classification
246
+ dataset:
247
+ type: mteb/mtop_intent
248
+ name: MTEB MTOPIntentClassification (fr)
249
+ config: fr
250
+ split: test
251
+ revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
252
+ metrics:
253
+ - type: accuracy
254
+ value: 60.87691825869088
255
+ - type: f1
256
+ value: 43.56160799721332
257
+ - task:
258
+ type: Classification
259
+ dataset:
260
+ type: masakhane/masakhanews
261
+ name: MTEB MasakhaNEWSClassification (fra)
262
+ config: fra
263
+ split: test
264
+ revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
265
+ metrics:
266
+ - type: accuracy
267
+ value: 70.52132701421802
268
+ - type: f1
269
+ value: 66.7911493789742
270
+ - task:
271
+ type: Clustering
272
+ dataset:
273
+ type: masakhane/masakhanews
274
+ name: MTEB MasakhaNEWSClusteringP2P (fra)
275
+ config: fra
276
+ split: test
277
+ revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
278
+ metrics:
279
+ - type: v_measure
280
+ value: 34.60975901092521
281
+ - task:
282
+ type: Clustering
283
+ dataset:
284
+ type: masakhane/masakhanews
285
+ name: MTEB MasakhaNEWSClusteringS2S (fra)
286
+ config: fra
287
+ split: test
288
+ revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
289
+ metrics:
290
+ - type: v_measure
291
+ value: 32.8092912406207
292
+ - task:
293
+ type: Classification
294
+ dataset:
295
+ type: mteb/amazon_massive_intent
296
+ name: MTEB MassiveIntentClassification (fr)
297
+ config: fr
298
+ split: test
299
+ revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
300
+ metrics:
301
+ - type: accuracy
302
+ value: 66.70477471418964
303
+ - type: f1
304
+ value: 64.4848306188641
305
+ - task:
306
+ type: Classification
307
+ dataset:
308
+ type: mteb/amazon_massive_scenario
309
+ name: MTEB MassiveScenarioClassification (fr)
310
+ config: fr
311
+ split: test
312
+ revision: 7d571f92784cd94a019292a1f45445077d0ef634
313
+ metrics:
314
+ - type: accuracy
315
+ value: 74.57969065232011
316
+ - type: f1
317
+ value: 73.58251655418402
318
+ - task:
319
+ type: Retrieval
320
+ dataset:
321
+ type: jinaai/mintakaqa
322
+ name: MTEB MintakaRetrieval (fr)
323
+ config: fr
324
+ split: test
325
+ revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
326
+ metrics:
327
+ - type: map_at_1
328
+ value: 14.005
329
+ - type: map_at_10
330
+ value: 21.279999999999998
331
+ - type: map_at_100
332
+ value: 22.288
333
+ - type: map_at_1000
334
+ value: 22.404
335
+ - type: map_at_3
336
+ value: 19.151
337
+ - type: map_at_5
338
+ value: 20.322000000000003
339
+ - type: mrr_at_1
340
+ value: 14.005
341
+ - type: mrr_at_10
342
+ value: 21.279999999999998
343
+ - type: mrr_at_100
344
+ value: 22.288
345
+ - type: mrr_at_1000
346
+ value: 22.404
347
+ - type: mrr_at_3
348
+ value: 19.151
349
+ - type: mrr_at_5
350
+ value: 20.322000000000003
351
+ - type: ndcg_at_1
352
+ value: 14.005
353
+ - type: ndcg_at_10
354
+ value: 25.173000000000002
355
+ - type: ndcg_at_100
356
+ value: 30.452
357
+ - type: ndcg_at_1000
358
+ value: 34.241
359
+ - type: ndcg_at_3
360
+ value: 20.768
361
+ - type: ndcg_at_5
362
+ value: 22.869
363
+ - type: precision_at_1
364
+ value: 14.005
365
+ - type: precision_at_10
366
+ value: 3.759
367
+ - type: precision_at_100
368
+ value: 0.631
369
+ - type: precision_at_1000
370
+ value: 0.095
371
+ - type: precision_at_3
372
+ value: 8.477
373
+ - type: precision_at_5
374
+ value: 6.101999999999999
375
+ - type: recall_at_1
376
+ value: 14.005
377
+ - type: recall_at_10
378
+ value: 37.592
379
+ - type: recall_at_100
380
+ value: 63.144999999999996
381
+ - type: recall_at_1000
382
+ value: 94.513
383
+ - type: recall_at_3
384
+ value: 25.430000000000003
385
+ - type: recall_at_5
386
+ value: 30.508000000000003
387
+ - task:
388
+ type: PairClassification
389
+ dataset:
390
+ type: GEM/opusparcus
391
+ name: MTEB OpusparcusPC (fr)
392
+ config: fr
393
+ split: test
394
+ revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
395
+ metrics:
396
+ - type: cos_sim_accuracy
397
+ value: 81.60762942779292
398
+ - type: cos_sim_ap
399
+ value: 93.33850264444463
400
+ - type: cos_sim_f1
401
+ value: 87.24705882352941
402
+ - type: cos_sim_precision
403
+ value: 82.91592128801432
404
+ - type: cos_sim_recall
405
+ value: 92.05561072492551
406
+ - type: dot_accuracy
407
+ value: 81.60762942779292
408
+ - type: dot_ap
409
+ value: 93.33850264444463
410
+ - type: dot_f1
411
+ value: 87.24705882352941
412
+ - type: dot_precision
413
+ value: 82.91592128801432
414
+ - type: dot_recall
415
+ value: 92.05561072492551
416
+ - type: euclidean_accuracy
417
+ value: 81.60762942779292
418
+ - type: euclidean_ap
419
+ value: 93.3384939260791
420
+ - type: euclidean_f1
421
+ value: 87.24705882352941
422
+ - type: euclidean_precision
423
+ value: 82.91592128801432
424
+ - type: euclidean_recall
425
+ value: 92.05561072492551
426
+ - type: manhattan_accuracy
427
+ value: 81.60762942779292
428
+ - type: manhattan_ap
429
+ value: 93.27064794794664
430
+ - type: manhattan_f1
431
+ value: 87.27440999537251
432
+ - type: manhattan_precision
433
+ value: 81.7157712305026
434
+ - type: manhattan_recall
435
+ value: 93.64448857994041
436
+ - type: max_accuracy
437
+ value: 81.60762942779292
438
+ - type: max_ap
439
+ value: 93.33850264444463
440
+ - type: max_f1
441
+ value: 87.27440999537251
442
+ - task:
443
+ type: PairClassification
444
+ dataset:
445
+ type: paws-x
446
+ name: MTEB PawsX (fr)
447
+ config: fr
448
+ split: test
449
+ revision: 8a04d940a42cd40658986fdd8e3da561533a3646
450
+ metrics:
451
+ - type: cos_sim_accuracy
452
+ value: 61.95
453
+ - type: cos_sim_ap
454
+ value: 60.8497942066519
455
+ - type: cos_sim_f1
456
+ value: 62.53032928942807
457
+ - type: cos_sim_precision
458
+ value: 45.50958627648839
459
+ - type: cos_sim_recall
460
+ value: 99.88925802879291
461
+ - type: dot_accuracy
462
+ value: 61.95
463
+ - type: dot_ap
464
+ value: 60.83772617132806
465
+ - type: dot_f1
466
+ value: 62.53032928942807
467
+ - type: dot_precision
468
+ value: 45.50958627648839
469
+ - type: dot_recall
470
+ value: 99.88925802879291
471
+ - type: euclidean_accuracy
472
+ value: 61.95
473
+ - type: euclidean_ap
474
+ value: 60.8497942066519
475
+ - type: euclidean_f1
476
+ value: 62.53032928942807
477
+ - type: euclidean_precision
478
+ value: 45.50958627648839
479
+ - type: euclidean_recall
480
+ value: 99.88925802879291
481
+ - type: manhattan_accuracy
482
+ value: 61.9
483
+ - type: manhattan_ap
484
+ value: 60.87914286416435
485
+ - type: manhattan_f1
486
+ value: 62.491349480968864
487
+ - type: manhattan_precision
488
+ value: 45.44539506794162
489
+ - type: manhattan_recall
490
+ value: 100.0
491
+ - type: max_accuracy
492
+ value: 61.95
493
+ - type: max_ap
494
+ value: 60.87914286416435
495
+ - type: max_f1
496
+ value: 62.53032928942807
497
+ - task:
498
+ type: STS
499
+ dataset:
500
+ type: Lajavaness/SICK-fr
501
+ name: MTEB SICKFr
502
+ config: default
503
+ split: test
504
+ revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
505
+ metrics:
506
+ - type: cos_sim_pearson
507
+ value: 81.24400370393097
508
+ - type: cos_sim_spearman
509
+ value: 75.50548831172674
510
+ - type: euclidean_pearson
511
+ value: 77.81039134726188
512
+ - type: euclidean_spearman
513
+ value: 75.50504199480463
514
+ - type: manhattan_pearson
515
+ value: 77.79383923445839
516
+ - type: manhattan_spearman
517
+ value: 75.472882776806
518
+ - task:
519
+ type: STS
520
+ dataset:
521
+ type: mteb/sts22-crosslingual-sts
522
+ name: MTEB STS22 (fr)
523
+ config: fr
524
+ split: test
525
+ revision: eea2b4fe26a775864c896887d910b76a8098ad3f
526
+ metrics:
527
+ - type: cos_sim_pearson
528
+ value: 80.48474973785514
529
+ - type: cos_sim_spearman
530
+ value: 81.69566405041475
531
+ - type: euclidean_pearson
532
+ value: 78.32784472269549
533
+ - type: euclidean_spearman
534
+ value: 81.69566405041475
535
+ - type: manhattan_pearson
536
+ value: 78.2856100079857
537
+ - type: manhattan_spearman
538
+ value: 81.84463256785325
539
+ - task:
540
+ type: STS
541
+ dataset:
542
+ type: PhilipMay/stsb_multi_mt
543
+ name: MTEB STSBenchmarkMultilingualSTS (fr)
544
+ config: fr
545
+ split: test
546
+ revision: 93d57ef91790589e3ce9c365164337a8a78b7632
547
+ metrics:
548
+ - type: cos_sim_pearson
549
+ value: 80.68785966129913
550
+ - type: cos_sim_spearman
551
+ value: 81.29936344904975
552
+ - type: euclidean_pearson
553
+ value: 80.25462090186443
554
+ - type: euclidean_spearman
555
+ value: 81.29928746010391
556
+ - type: manhattan_pearson
557
+ value: 80.17083094559602
558
+ - type: manhattan_spearman
559
+ value: 81.18921827402406
560
+ - task:
561
+ type: Summarization
562
+ dataset:
563
+ type: lyon-nlp/summarization-summeval-fr-p2p
564
+ name: MTEB SummEvalFr
565
+ config: default
566
+ split: test
567
+ revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
568
+ metrics:
569
+ - type: cos_sim_pearson
570
+ value: 31.66113105701837
571
+ - type: cos_sim_spearman
572
+ value: 30.13316633681715
573
+ - type: dot_pearson
574
+ value: 31.66113064418324
575
+ - type: dot_spearman
576
+ value: 30.13316633681715
577
+ - task:
578
+ type: Reranking
579
+ dataset:
580
+ type: lyon-nlp/mteb-fr-reranking-syntec-s2p
581
+ name: MTEB SyntecReranking
582
+ config: default
583
+ split: test
584
+ revision: b205c5084a0934ce8af14338bf03feb19499c84d
585
+ metrics:
586
+ - type: map
587
+ value: 85.43333333333334
588
+ - type: mrr
589
+ value: 85.43333333333334
590
+ - task:
591
+ type: Retrieval
592
+ dataset:
593
+ type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
594
+ name: MTEB SyntecRetrieval
595
+ config: default
596
+ split: test
597
+ revision: aa460cd4d177e6a3c04fcd2affd95e8243289033
598
+ metrics:
599
+ - type: map_at_1
600
+ value: 65.0
601
+ - type: map_at_10
602
+ value: 75.19200000000001
603
+ - type: map_at_100
604
+ value: 75.77000000000001
605
+ - type: map_at_1000
606
+ value: 75.77000000000001
607
+ - type: map_at_3
608
+ value: 73.667
609
+ - type: map_at_5
610
+ value: 75.067
611
+ - type: mrr_at_1
612
+ value: 65.0
613
+ - type: mrr_at_10
614
+ value: 75.19200000000001
615
+ - type: mrr_at_100
616
+ value: 75.77000000000001
617
+ - type: mrr_at_1000
618
+ value: 75.77000000000001
619
+ - type: mrr_at_3
620
+ value: 73.667
621
+ - type: mrr_at_5
622
+ value: 75.067
623
+ - type: ndcg_at_1
624
+ value: 65.0
625
+ - type: ndcg_at_10
626
+ value: 79.145
627
+ - type: ndcg_at_100
628
+ value: 81.34400000000001
629
+ - type: ndcg_at_1000
630
+ value: 81.34400000000001
631
+ - type: ndcg_at_3
632
+ value: 76.333
633
+ - type: ndcg_at_5
634
+ value: 78.82900000000001
635
+ - type: precision_at_1
636
+ value: 65.0
637
+ - type: precision_at_10
638
+ value: 9.1
639
+ - type: precision_at_100
640
+ value: 1.0
641
+ - type: precision_at_1000
642
+ value: 0.1
643
+ - type: precision_at_3
644
+ value: 28.000000000000004
645
+ - type: precision_at_5
646
+ value: 18.0
647
+ - type: recall_at_1
648
+ value: 65.0
649
+ - type: recall_at_10
650
+ value: 91.0
651
+ - type: recall_at_100
652
+ value: 100.0
653
+ - type: recall_at_1000
654
+ value: 100.0
655
+ - type: recall_at_3
656
+ value: 84.0
657
+ - type: recall_at_5
658
+ value: 90.0
659
+ - task:
660
+ type: Retrieval
661
+ dataset:
662
+ type: jinaai/xpqa
663
+ name: MTEB XPQARetrieval (fr)
664
+ config: fr
665
+ split: test
666
+ revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
667
+ metrics:
668
+ - type: map_at_1
669
+ value: 40.225
670
+ - type: map_at_10
671
+ value: 61.833000000000006
672
+ - type: map_at_100
673
+ value: 63.20400000000001
674
+ - type: map_at_1000
675
+ value: 63.27
676
+ - type: map_at_3
677
+ value: 55.593
678
+ - type: map_at_5
679
+ value: 59.65200000000001
680
+ - type: mrr_at_1
681
+ value: 63.284
682
+ - type: mrr_at_10
683
+ value: 71.351
684
+ - type: mrr_at_100
685
+ value: 71.772
686
+ - type: mrr_at_1000
687
+ value: 71.786
688
+ - type: mrr_at_3
689
+ value: 69.381
690
+ - type: mrr_at_5
691
+ value: 70.703
692
+ - type: ndcg_at_1
693
+ value: 63.284
694
+ - type: ndcg_at_10
695
+ value: 68.49199999999999
696
+ - type: ndcg_at_100
697
+ value: 72.79299999999999
698
+ - type: ndcg_at_1000
699
+ value: 73.735
700
+ - type: ndcg_at_3
701
+ value: 63.278
702
+ - type: ndcg_at_5
703
+ value: 65.19200000000001
704
+ - type: precision_at_1
705
+ value: 63.284
706
+ - type: precision_at_10
707
+ value: 15.661
708
+ - type: precision_at_100
709
+ value: 1.9349999999999998
710
+ - type: precision_at_1000
711
+ value: 0.207
712
+ - type: precision_at_3
713
+ value: 38.273
714
+ - type: precision_at_5
715
+ value: 27.397
716
+ - type: recall_at_1
717
+ value: 40.225
718
+ - type: recall_at_10
719
+ value: 77.66999999999999
720
+ - type: recall_at_100
721
+ value: 93.887
722
+ - type: recall_at_1000
723
+ value: 99.70599999999999
724
+ - type: recall_at_3
725
+ value: 61.133
726
+ - type: recall_at_5
727
+ value: 69.789
728
  ---
729
 
730
  # {MODEL_NAME}