pawasthy commited on
Commit
85d69fc
1 Parent(s): 8b8e49e

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +1788 -1789
README.md CHANGED
@@ -1,1794 +1,1793 @@
1
- ---
2
- language:
3
- - en
4
- license: apache-2.0
5
- library_name: transformers
6
- tags:
7
- - language
8
- - granite
9
- - embeddings
10
- model-index:
11
- - name: ibm-granite/granite-embedding-30m-english
12
- results:
13
- - dataset:
14
- type: mteb/arguana
15
- name: MTEB ArguaAna
16
- config: default
17
- split: test
18
- task:
19
- type: Retrieval
20
- metrics:
21
- - type: map_at_1
22
- value: 0.31792
23
- - type: map_at_10
24
- value: 0.47599
25
- - type: map_at_100
26
- value: 0.48425
27
- - type: map_at_1000
28
- value: 0.48427
29
- - type: map_at_3
30
- value: 0.42757
31
- - type: map_at_5
32
- value: 0.45634
33
- - type: mrr_at_1
34
- value: 0.32788
35
- - type: mrr_at_10
36
- value: 0.47974
37
- - type: mrr_at_100
38
- value: 0.48801
39
- - type: mrr_at_1000
40
- value: 0.48802
41
- - type: mrr_at_3
42
- value: 0.43065
43
- - type: mrr_at_5
44
- value: 0.45999
45
- - type: ndcg_at_1
46
- value: 0.31792
47
- - type: ndcg_at_10
48
- value: 0.56356
49
- - type: ndcg_at_100
50
- value: 0.59789
51
- - type: ndcg_at_1000
52
- value: 0.59857
53
- - type: ndcg_at_3
54
- value: 0.46453
55
- - type: ndcg_at_5
56
- value: 0.51623
57
- - type: precision_at_1
58
- value: 0.31792
59
- - type: precision_at_10
60
- value: 0.08428
61
- - type: precision_at_100
62
- value: 0.00991
63
- - type: precision_at_1000
64
- value: 0.001
65
- - type: precision_at_3
66
- value: 0.19061
67
- - type: precision_at_5
68
- value: 0.1394
69
- - type: recall_at_1
70
- value: 0.31792
71
- - type: recall_at_10
72
- value: 0.84282
73
- - type: recall_at_100
74
- value: 0.99075
75
- - type: recall_at_1000
76
- value: 0.99644
77
- - type: recall_at_3
78
- value: 0.57183
79
- - type: recall_at_5
80
- value: 0.69701
81
- - dataset:
82
- type: mteb/climate-fever
83
- name: MTEB ClimateFEVER
84
- config: default
85
- split: test
86
- task:
87
- type: Retrieval
88
- metrics:
89
- - type: map_at_1
90
- value: 0.13189
91
- - type: map_at_10
92
- value: 0.21789
93
- - type: map_at_100
94
- value: 0.2358
95
- - type: map_at_1000
96
- value: 0.23772
97
- - type: map_at_3
98
- value: 0.18513
99
- - type: map_at_5
100
- value: 0.20212
101
- - type: mrr_at_1
102
- value: 0.29837
103
- - type: mrr_at_10
104
- value: 0.41376
105
- - type: mrr_at_100
106
- value: 0.42282
107
- - type: mrr_at_1000
108
- value: 0.42319
109
- - type: mrr_at_3
110
- value: 0.38284
111
- - type: mrr_at_5
112
- value: 0.40301
113
- - type: ndcg_at_1
114
- value: 0.29837
115
- - type: ndcg_at_10
116
- value: 0.30263
117
- - type: ndcg_at_100
118
- value: 0.37228
119
- - type: ndcg_at_1000
120
- value: 0.40677
121
- - type: ndcg_at_3
122
- value: 0.25392
123
- - type: ndcg_at_5
124
- value: 0.27153
125
- - type: precision_at_1
126
- value: 0.29837
127
- - type: precision_at_10
128
- value: 0.09179
129
- - type: precision_at_100
130
- value: 0.01659
131
- - type: precision_at_1000
132
- value: 0.0023
133
- - type: precision_at_3
134
- value: 0.18545
135
- - type: precision_at_5
136
- value: 0.14241
137
- - type: recall_at_1
138
- value: 0.13189
139
- - type: recall_at_10
140
- value: 0.35355
141
- - type: recall_at_100
142
- value: 0.59255
143
- - type: recall_at_1000
144
- value: 0.78637
145
- - type: recall_at_3
146
- value: 0.23255
147
- - type: recall_at_5
148
- value: 0.28446
149
- - dataset:
150
- type: mteb/cqadupstack-android
151
- name: MTEB CQADupstackAndroidRetrieval
152
- config: default
153
- split: test
154
- task:
155
- type: Retrieval
156
- metrics:
157
- - type: map_at_1
158
- value: 0.35797
159
- - type: map_at_10
160
- value: 0.47793
161
- - type: map_at_100
162
- value: 0.49422
163
- - type: map_at_1000
164
- value: 0.49546
165
- - type: map_at_3
166
- value: 0.44137
167
- - type: map_at_5
168
- value: 0.46063
169
- - type: mrr_at_1
170
- value: 0.44206
171
- - type: mrr_at_10
172
- value: 0.53808
173
- - type: mrr_at_100
174
- value: 0.5454
175
- - type: mrr_at_1000
176
- value: 0.54578
177
- - type: mrr_at_3
178
- value: 0.51431
179
- - type: mrr_at_5
180
- value: 0.5284
181
- - type: ndcg_at_1
182
- value: 0.44206
183
- - type: ndcg_at_10
184
- value: 0.54106
185
- - type: ndcg_at_100
186
- value: 0.59335
187
- - type: ndcg_at_1000
188
- value: 0.61015
189
- - type: ndcg_at_3
190
- value: 0.49365
191
- - type: ndcg_at_5
192
- value: 0.51429
193
- - type: precision_at_1
194
- value: 0.44206
195
- - type: precision_at_10
196
- value: 0.10443
197
- - type: precision_at_100
198
- value: 0.01631
199
- - type: precision_at_1000
200
- value: 0.00214
201
- - type: precision_at_3
202
- value: 0.23653
203
- - type: precision_at_5
204
- value: 0.1691
205
- - type: recall_at_1
206
- value: 0.35797
207
- - type: recall_at_10
208
- value: 0.65182
209
- - type: recall_at_100
210
- value: 0.86654
211
- - type: recall_at_1000
212
- value: 0.97131
213
- - type: recall_at_3
214
- value: 0.51224
215
- - type: recall_at_5
216
- value: 0.57219
217
- - dataset:
218
- type: mteb/cqadupstack-english
219
- name: MTEB CQADupstackEnglishRetrieval
220
- config: default
221
- split: test
222
- task:
223
- type: Retrieval
224
- metrics:
225
- - type: map_at_1
226
- value: 0.32748
227
- - type: map_at_10
228
- value: 0.44138
229
- - type: map_at_100
230
- value: 0.45565
231
- - type: map_at_1000
232
- value: 0.45698
233
- - type: map_at_3
234
- value: 0.40916
235
- - type: map_at_5
236
- value: 0.42621
237
- - type: mrr_at_1
238
- value: 0.41274
239
- - type: mrr_at_10
240
- value: 0.5046
241
- - type: mrr_at_100
242
- value: 0.5107
243
- - type: mrr_at_1000
244
- value: 0.51109
245
- - type: mrr_at_3
246
- value: 0.48238
247
- - type: mrr_at_5
248
- value: 0.49563
249
- - type: ndcg_at_1
250
- value: 0.41274
251
- - type: ndcg_at_10
252
- value: 0.50251
253
- - type: ndcg_at_100
254
- value: 0.54725
255
- - type: ndcg_at_1000
256
- value: 0.56635
257
- - type: ndcg_at_3
258
- value: 0.46023
259
- - type: ndcg_at_5
260
- value: 0.47883
261
- - type: precision_at_1
262
- value: 0.41274
263
- - type: precision_at_10
264
- value: 0.09828
265
- - type: precision_at_100
266
- value: 0.01573
267
- - type: precision_at_1000
268
- value: 0.00202
269
- - type: precision_at_3
270
- value: 0.22718
271
- - type: precision_at_5
272
- value: 0.16064
273
- - type: recall_at_1
274
- value: 0.32748
275
- - type: recall_at_10
276
- value: 0.60322
277
- - type: recall_at_100
278
- value: 0.79669
279
- - type: recall_at_1000
280
- value: 0.9173
281
- - type: recall_at_3
282
- value: 0.47523
283
- - type: recall_at_5
284
- value: 0.52957
285
- - dataset:
286
- type: mteb/cqadupstack-gaming
287
- name: MTEB CQADupstackGamingRetrieval
288
- config: default
289
- split: test
290
- task:
291
- type: Retrieval
292
- metrics:
293
- - type: map_at_1
294
- value: 0.41126
295
- - type: map_at_10
296
- value: 0.53661
297
- - type: map_at_100
298
- value: 0.54588
299
- - type: map_at_1000
300
- value: 0.54638
301
- - type: map_at_3
302
- value: 0.50389
303
- - type: map_at_5
304
- value: 0.52286
305
- - type: mrr_at_1
306
- value: 0.47147
307
- - type: mrr_at_10
308
- value: 0.5685
309
- - type: mrr_at_100
310
- value: 0.57458
311
- - type: mrr_at_1000
312
- value: 0.57487
313
- - type: mrr_at_3
314
- value: 0.54431
315
- - type: mrr_at_5
316
- value: 0.55957
317
- - type: ndcg_at_1
318
- value: 0.47147
319
- - type: ndcg_at_10
320
- value: 0.59318
321
- - type: ndcg_at_100
322
- value: 0.62972
323
- - type: ndcg_at_1000
324
- value: 0.64033
325
- - type: ndcg_at_3
326
- value: 0.53969
327
- - type: ndcg_at_5
328
- value: 0.56743
329
- - type: precision_at_1
330
- value: 0.47147
331
- - type: precision_at_10
332
- value: 0.09549
333
- - type: precision_at_100
334
- value: 0.01224
335
- - type: precision_at_1000
336
- value: 0.00135
337
- - type: precision_at_3
338
- value: 0.24159
339
- - type: precision_at_5
340
- value: 0.16577
341
- - type: recall_at_1
342
- value: 0.41126
343
- - type: recall_at_10
344
- value: 0.72691
345
- - type: recall_at_100
346
- value: 0.88692
347
- - type: recall_at_1000
348
- value: 0.96232
349
- - type: recall_at_3
350
- value: 0.58374
351
- - type: recall_at_5
352
- value: 0.65226
353
- - dataset:
354
- type: mteb/cqadupstack-gis
355
- name: MTEB CQADupstackGisRetrieval
356
- config: default
357
- split: test
358
- task:
359
- type: Retrieval
360
- metrics:
361
- - type: map_at_1
362
- value: 0.28464
363
- - type: map_at_10
364
- value: 0.3828
365
- - type: map_at_100
366
- value: 0.39277
367
- - type: map_at_1000
368
- value: 0.39355
369
- - type: map_at_3
370
- value: 0.35704
371
- - type: map_at_5
372
- value: 0.37116
373
- - type: mrr_at_1
374
- value: 0.30734
375
- - type: mrr_at_10
376
- value: 0.40422
377
- - type: mrr_at_100
378
- value: 0.41297
379
- - type: mrr_at_1000
380
- value: 0.41355
381
- - type: mrr_at_3
382
- value: 0.38136
383
- - type: mrr_at_5
384
- value: 0.39362
385
- - type: ndcg_at_1
386
- value: 0.30734
387
- - type: ndcg_at_10
388
- value: 0.43564
389
- - type: ndcg_at_100
390
- value: 0.48419
391
- - type: ndcg_at_1000
392
- value: 0.50404
393
- - type: ndcg_at_3
394
- value: 0.38672
395
- - type: ndcg_at_5
396
- value: 0.40954
397
- - type: precision_at_1
398
- value: 0.30734
399
- - type: precision_at_10
400
- value: 0.06633
401
- - type: precision_at_100
402
- value: 0.00956
403
- - type: precision_at_1000
404
- value: 0.00116
405
- - type: precision_at_3
406
- value: 0.16497
407
- - type: precision_at_5
408
- value: 0.11254
409
- - type: recall_at_1
410
- value: 0.28464
411
- - type: recall_at_10
412
- value: 0.57621
413
- - type: recall_at_100
414
- value: 0.7966
415
- - type: recall_at_1000
416
- value: 0.94633
417
- - type: recall_at_3
418
- value: 0.44588
419
- - type: recall_at_5
420
- value: 0.50031
421
- - dataset:
422
- type: mteb/cqadupstack-mathematica
423
- name: MTEB CQADupstackMathematicaRetrieval
424
- config: default
425
- split: test
426
- task:
427
- type: Retrieval
428
- metrics:
429
- - type: map_at_1
430
- value: 0.18119
431
- - type: map_at_10
432
- value: 0.27055
433
- - type: map_at_100
434
- value: 0.28461
435
- - type: map_at_1000
436
- value: 0.28577
437
- - type: map_at_3
438
- value: 0.24341
439
- - type: map_at_5
440
- value: 0.25861
441
- - type: mrr_at_1
442
- value: 0.22886
443
- - type: mrr_at_10
444
- value: 0.32234
445
- - type: mrr_at_100
446
- value: 0.3328
447
- - type: mrr_at_1000
448
- value: 0.3334
449
- - type: mrr_at_3
450
- value: 0.29664
451
- - type: mrr_at_5
452
- value: 0.31107
453
- - type: ndcg_at_1
454
- value: 0.22886
455
- - type: ndcg_at_10
456
- value: 0.32749
457
- - type: ndcg_at_100
458
- value: 0.39095
459
- - type: ndcg_at_1000
460
- value: 0.41656
461
- - type: ndcg_at_3
462
- value: 0.27864
463
- - type: ndcg_at_5
464
- value: 0.30177
465
- - type: precision_at_1
466
- value: 0.22886
467
- - type: precision_at_10
468
- value: 0.06169
469
- - type: precision_at_100
470
- value: 0.0107
471
- - type: precision_at_1000
472
- value: 0.00143
473
- - type: precision_at_3
474
- value: 0.13682
475
- - type: precision_at_5
476
- value: 0.0995
477
- - type: recall_at_1
478
- value: 0.18119
479
- - type: recall_at_10
480
- value: 0.44983
481
- - type: recall_at_100
482
- value: 0.72396
483
- - type: recall_at_1000
484
- value: 0.90223
485
- - type: recall_at_3
486
- value: 0.31633
487
- - type: recall_at_5
488
- value: 0.37532
489
- - dataset:
490
- type: mteb/cqadupstack-physics
491
- name: MTEB CQADupstackPhysicsRetrieval
492
- config: default
493
- split: test
494
- task:
495
- type: Retrieval
496
- metrics:
497
- - type: map_at_1
498
- value: 0.30517
499
- - type: map_at_10
500
- value: 0.42031
501
- - type: map_at_100
502
- value: 0.43415
503
- - type: map_at_1000
504
- value: 0.43525
505
- - type: map_at_3
506
- value: 0.38443
507
- - type: map_at_5
508
- value: 0.40685
509
- - type: mrr_at_1
510
- value: 0.38114
511
- - type: mrr_at_10
512
- value: 0.47783
513
- - type: mrr_at_100
514
- value: 0.48647
515
- - type: mrr_at_1000
516
- value: 0.48688
517
- - type: mrr_at_3
518
- value: 0.45172
519
- - type: mrr_at_5
520
- value: 0.46817
521
- - type: ndcg_at_1
522
- value: 0.38114
523
- - type: ndcg_at_10
524
- value: 0.4834
525
- - type: ndcg_at_100
526
- value: 0.53861
527
- - type: ndcg_at_1000
528
- value: 0.55701
529
- - type: ndcg_at_3
530
- value: 0.42986
531
- - type: ndcg_at_5
532
- value: 0.45893
533
- - type: precision_at_1
534
- value: 0.38114
535
- - type: precision_at_10
536
- value: 0.08893
537
- - type: precision_at_100
538
- value: 0.01375
539
- - type: precision_at_1000
540
- value: 0.00172
541
- - type: precision_at_3
542
- value: 0.20821
543
- - type: precision_at_5
544
- value: 0.15034
545
- - type: recall_at_1
546
- value: 0.30517
547
- - type: recall_at_10
548
- value: 0.61332
549
- - type: recall_at_100
550
- value: 0.84051
551
- - type: recall_at_1000
552
- value: 0.95826
553
- - type: recall_at_3
554
- value: 0.46015
555
- - type: recall_at_5
556
- value: 0.53801
557
- - dataset:
558
- type: mteb/cqadupstack-programmers
559
- name: MTEB CQADupstackProgrammersRetrieval
560
- config: default
561
- split: test
562
- task:
563
- type: Retrieval
564
- metrics:
565
- - type: map_at_1
566
- value: 0.27396
567
- - type: map_at_10
568
- value: 0.38043
569
- - type: map_at_100
570
- value: 0.39341
571
- - type: map_at_1000
572
- value: 0.39454
573
- - type: map_at_3
574
- value: 0.34783
575
- - type: map_at_5
576
- value: 0.3663
577
- - type: mrr_at_1
578
- value: 0.34247
579
- - type: mrr_at_10
580
- value: 0.43681
581
- - type: mrr_at_100
582
- value: 0.4451
583
- - type: mrr_at_1000
584
- value: 0.44569
585
- - type: mrr_at_3
586
- value: 0.41172
587
- - type: mrr_at_5
588
- value: 0.42702
589
- - type: ndcg_at_1
590
- value: 0.34247
591
- - type: ndcg_at_10
592
- value: 0.44065
593
- - type: ndcg_at_100
594
- value: 0.49434
595
- - type: ndcg_at_1000
596
- value: 0.51682
597
- - type: ndcg_at_3
598
- value: 0.38976
599
- - type: ndcg_at_5
600
- value: 0.41332
601
- - type: precision_at_1
602
- value: 0.34247
603
- - type: precision_at_10
604
- value: 0.08059
605
- - type: precision_at_100
606
- value: 0.01258
607
- - type: precision_at_1000
608
- value: 0.00162
609
- - type: precision_at_3
610
- value: 0.1876
611
- - type: precision_at_5
612
- value: 0.13333
613
- - type: recall_at_1
614
- value: 0.27396
615
- - type: recall_at_10
616
- value: 0.56481
617
- - type: recall_at_100
618
- value: 0.79012
619
- - type: recall_at_1000
620
- value: 0.94182
621
- - type: recall_at_3
622
- value: 0.41785
623
- - type: recall_at_5
624
- value: 0.48303
625
- - dataset:
626
- type: mteb/cqadupstack-stats
627
- name: MTEB CQADupstackStatsRetrieval
628
- config: default
629
- split: test
630
- task:
631
- type: Retrieval
632
- metrics:
633
- - type: map_at_1
634
- value: 0.25728
635
- - type: map_at_10
636
- value: 0.33903
637
- - type: map_at_100
638
- value: 0.34853
639
- - type: map_at_1000
640
- value: 0.34944
641
- - type: map_at_3
642
- value: 0.31268
643
- - type: map_at_5
644
- value: 0.32596
645
- - type: mrr_at_1
646
- value: 0.29141
647
- - type: mrr_at_10
648
- value: 0.36739
649
- - type: mrr_at_100
650
- value: 0.37545
651
- - type: mrr_at_1000
652
- value: 0.37608
653
- - type: mrr_at_3
654
- value: 0.34407
655
- - type: mrr_at_5
656
- value: 0.3568
657
- - type: ndcg_at_1
658
- value: 0.29141
659
- - type: ndcg_at_10
660
- value: 0.38596
661
- - type: ndcg_at_100
662
- value: 0.43375
663
- - type: ndcg_at_1000
664
- value: 0.45562
665
- - type: ndcg_at_3
666
- value: 0.33861
667
- - type: ndcg_at_5
668
- value: 0.35887
669
- - type: precision_at_1
670
- value: 0.29141
671
- - type: precision_at_10
672
- value: 0.06334
673
- - type: precision_at_100
674
- value: 0.00952
675
- - type: precision_at_1000
676
- value: 0.00121
677
- - type: precision_at_3
678
- value: 0.14826
679
- - type: precision_at_5
680
- value: 0.10429
681
- - type: recall_at_1
682
- value: 0.25728
683
- - type: recall_at_10
684
- value: 0.50121
685
- - type: recall_at_100
686
- value: 0.72382
687
- - type: recall_at_1000
688
- value: 0.88306
689
- - type: recall_at_3
690
- value: 0.36638
691
- - type: recall_at_5
692
- value: 0.41689
693
- - dataset:
694
- type: mteb/cqadupstack-tex
695
- name: MTEB CQADupstackTexRetrieval
696
- config: default
697
- split: test
698
- task:
699
- type: Retrieval
700
- metrics:
701
- - type: map_at_1
702
- value: 0.19911
703
- - type: map_at_10
704
- value: 0.2856
705
- - type: map_at_100
706
- value: 0.29785
707
- - type: map_at_1000
708
- value: 0.29911
709
- - type: map_at_3
710
- value: 0.25875
711
- - type: map_at_5
712
- value: 0.2741
713
- - type: mrr_at_1
714
- value: 0.24054
715
- - type: mrr_at_10
716
- value: 0.32483
717
- - type: mrr_at_100
718
- value: 0.33464
719
- - type: mrr_at_1000
720
- value: 0.33534
721
- - type: mrr_at_3
722
- value: 0.30162
723
- - type: mrr_at_5
724
- value: 0.31506
725
- - type: ndcg_at_1
726
- value: 0.24054
727
- - type: ndcg_at_10
728
- value: 0.33723
729
- - type: ndcg_at_100
730
- value: 0.39362
731
- - type: ndcg_at_1000
732
- value: 0.42065
733
- - type: ndcg_at_3
734
- value: 0.29116
735
- - type: ndcg_at_5
736
- value: 0.31299
737
- - type: precision_at_1
738
- value: 0.24054
739
- - type: precision_at_10
740
- value: 0.06194
741
- - type: precision_at_100
742
- value: 0.01058
743
- - type: precision_at_1000
744
- value: 0.00148
745
- - type: precision_at_3
746
- value: 0.13914
747
- - type: precision_at_5
748
- value: 0.10076
749
- - type: recall_at_1
750
- value: 0.19911
751
- - type: recall_at_10
752
- value: 0.45183
753
- - type: recall_at_100
754
- value: 0.7025
755
- - type: recall_at_1000
756
- value: 0.89222
757
- - type: recall_at_3
758
- value: 0.32195
759
- - type: recall_at_5
760
- value: 0.37852
761
- - dataset:
762
- type: mteb/cqadupstack-unix
763
- name: MTEB CQADupstackUnixRetrieval
764
- config: default
765
- split: test
766
- task:
767
- type: Retrieval
768
- metrics:
769
- - type: map_at_1
770
- value: 0.29819
771
- - type: map_at_10
772
- value: 0.40073
773
- - type: map_at_100
774
- value: 0.41289
775
- - type: map_at_1000
776
- value: 0.41375
777
- - type: map_at_3
778
- value: 0.36572
779
- - type: map_at_5
780
- value: 0.38386
781
- - type: mrr_at_1
782
- value: 0.35168
783
- - type: mrr_at_10
784
- value: 0.44381
785
- - type: mrr_at_100
786
- value: 0.45191
787
- - type: mrr_at_1000
788
- value: 0.45234
789
- - type: mrr_at_3
790
- value: 0.41402
791
- - type: mrr_at_5
792
- value: 0.43039
793
- - type: ndcg_at_1
794
- value: 0.35168
795
- - type: ndcg_at_10
796
- value: 0.46071
797
- - type: ndcg_at_100
798
- value: 0.51351
799
- - type: ndcg_at_1000
800
- value: 0.5317
801
- - type: ndcg_at_3
802
- value: 0.39972
803
- - type: ndcg_at_5
804
- value: 0.42586
805
- - type: precision_at_1
806
- value: 0.35168
807
- - type: precision_at_10
808
- value: 0.07985
809
- - type: precision_at_100
810
- value: 0.01185
811
- - type: precision_at_1000
812
- value: 0.00144
813
- - type: precision_at_3
814
- value: 0.18221
815
- - type: precision_at_5
816
- value: 0.12892
817
- - type: recall_at_1
818
- value: 0.29819
819
- - type: recall_at_10
820
- value: 0.60075
821
- - type: recall_at_100
822
- value: 0.82771
823
- - type: recall_at_1000
824
- value: 0.95219
825
- - type: recall_at_3
826
- value: 0.43245
827
- - type: recall_at_5
828
- value: 0.49931
829
- - dataset:
830
- type: mteb/cqadupstack-webmasters
831
- name: MTEB CQADupstackWebmastersRetrieval
832
- config: default
833
- split: test
834
- task:
835
- type: Retrieval
836
- metrics:
837
- - type: map_at_1
838
- value: 0.28409
839
- - type: map_at_10
840
- value: 0.37621
841
- - type: map_at_100
842
- value: 0.39233
843
- - type: map_at_1000
844
- value: 0.39471
845
- - type: map_at_3
846
- value: 0.34337
847
- - type: map_at_5
848
- value: 0.35985
849
- - type: mrr_at_1
850
- value: 0.33794
851
- - type: mrr_at_10
852
- value: 0.42349
853
- - type: mrr_at_100
854
- value: 0.43196
855
- - type: mrr_at_1000
856
- value: 0.43237
857
- - type: mrr_at_3
858
- value: 0.39526
859
- - type: mrr_at_5
860
- value: 0.41087
861
- - type: ndcg_at_1
862
- value: 0.33794
863
- - type: ndcg_at_10
864
- value: 0.43832
865
- - type: ndcg_at_100
866
- value: 0.49514
867
- - type: ndcg_at_1000
868
- value: 0.51742
869
- - type: ndcg_at_3
870
- value: 0.38442
871
- - type: ndcg_at_5
872
- value: 0.40737
873
- - type: precision_at_1
874
- value: 0.33794
875
- - type: precision_at_10
876
- value: 0.08597
877
- - type: precision_at_100
878
- value: 0.01652
879
- - type: precision_at_1000
880
- value: 0.00251
881
- - type: precision_at_3
882
- value: 0.17787
883
- - type: precision_at_5
884
- value: 0.13241
885
- - type: recall_at_1
886
- value: 0.28409
887
- - type: recall_at_10
888
- value: 0.55388
889
- - type: recall_at_100
890
- value: 0.81517
891
- - type: recall_at_1000
892
- value: 0.95038
893
- - type: recall_at_3
894
- value: 0.40133
895
- - type: recall_at_5
896
- value: 0.45913
897
- - dataset:
898
- type: mteb/cqadupstack-wordpress
899
- name: MTEB CQADupstackWordpressRetrieval
900
- config: default
901
- split: test
902
- task:
903
- type: Retrieval
904
- metrics:
905
- - type: map_at_1
906
- value: 0.24067
907
- - type: map_at_10
908
- value: 0.32184
909
- - type: map_at_100
910
- value: 0.33357
911
- - type: map_at_1000
912
- value: 0.33458
913
- - type: map_at_3
914
- value: 0.29492
915
- - type: map_at_5
916
- value: 0.3111
917
- - type: mrr_at_1
918
- value: 0.26248
919
- - type: mrr_at_10
920
- value: 0.34149
921
- - type: mrr_at_100
922
- value: 0.35189
923
- - type: mrr_at_1000
924
- value: 0.35251
925
- - type: mrr_at_3
926
- value: 0.31639
927
- - type: mrr_at_5
928
- value: 0.33182
929
- - type: ndcg_at_1
930
- value: 0.26248
931
- - type: ndcg_at_10
932
- value: 0.36889
933
- - type: ndcg_at_100
934
- value: 0.42426
935
- - type: ndcg_at_1000
936
- value: 0.44745
937
- - type: ndcg_at_3
938
- value: 0.31799
939
- - type: ndcg_at_5
940
- value: 0.34563
941
- - type: precision_at_1
942
- value: 0.26248
943
- - type: precision_at_10
944
- value: 0.05712
945
- - type: precision_at_100
946
- value: 0.00915
947
- - type: precision_at_1000
948
- value: 0.00123
949
- - type: precision_at_3
950
- value: 0.13309
951
- - type: precision_at_5
952
- value: 0.09649
953
- - type: recall_at_1
954
- value: 0.24067
955
- - type: recall_at_10
956
- value: 0.49344
957
- - type: recall_at_100
958
- value: 0.7412
959
- - type: recall_at_1000
960
- value: 0.91276
961
- - type: recall_at_3
962
- value: 0.36272
963
- - type: recall_at_5
964
- value: 0.4277
965
- - dataset:
966
- type: mteb/dbpedia
967
- name: MTEB DBPedia
968
- config: default
969
- split: test
970
- task:
971
- type: Retrieval
972
- metrics:
973
- - type: map_at_1
974
- value: 0.08651
975
- - type: map_at_10
976
- value: 0.17628
977
- - type: map_at_100
978
- value: 0.23354
979
- - type: map_at_1000
980
- value: 0.24827
981
- - type: map_at_3
982
- value: 0.1351
983
- - type: map_at_5
984
- value: 0.15468
985
- - type: mrr_at_1
986
- value: 0.645
987
- - type: mrr_at_10
988
- value: 0.71989
989
- - type: mrr_at_100
990
- value: 0.72332
991
- - type: mrr_at_1000
992
- value: 0.72346
993
- - type: mrr_at_3
994
- value: 0.7025
995
- - type: mrr_at_5
996
- value: 0.71275
997
- - type: ndcg_at_1
998
- value: 0.51375
999
- - type: ndcg_at_10
1000
- value: 0.3596
1001
- - type: ndcg_at_100
1002
- value: 0.39878
1003
- - type: ndcg_at_1000
1004
- value: 0.47931
1005
- - type: ndcg_at_3
1006
- value: 0.41275
1007
- - type: ndcg_at_5
1008
- value: 0.38297
1009
- - type: precision_at_1
1010
- value: 0.645
1011
- - type: precision_at_10
1012
- value: 0.2745
1013
- - type: precision_at_100
1014
- value: 0.08405
1015
- - type: precision_at_1000
1016
- value: 0.01923
1017
- - type: precision_at_3
1018
- value: 0.44417
1019
- - type: precision_at_5
1020
- value: 0.366
1021
- - type: recall_at_1
1022
- value: 0.08651
1023
- - type: recall_at_10
1024
- value: 0.22416
1025
- - type: recall_at_100
1026
- value: 0.46381
1027
- - type: recall_at_1000
1028
- value: 0.71557
1029
- - type: recall_at_3
1030
- value: 0.14847
1031
- - type: recall_at_5
1032
- value: 0.1804
1033
- - dataset:
1034
- type: mteb/fever
1035
- name: MTEB FEVER
1036
- config: default
1037
- split: test
1038
- task:
1039
- type: Retrieval
1040
- metrics:
1041
- - type: map_at_1
1042
- value: 0.73211
1043
- - type: map_at_10
1044
- value: 0.81463
1045
- - type: map_at_100
1046
- value: 0.81622
1047
- - type: map_at_1000
1048
- value: 0.81634
1049
- - type: map_at_3
1050
- value: 0.805
1051
- - type: map_at_5
1052
- value: 0.81134
1053
- - type: mrr_at_1
1054
- value: 0.79088
1055
- - type: mrr_at_10
1056
- value: 0.86943
1057
- - type: mrr_at_100
1058
- value: 0.87017
1059
- - type: mrr_at_1000
1060
- value: 0.87018
1061
- - type: mrr_at_3
1062
- value: 0.86154
1063
- - type: mrr_at_5
1064
- value: 0.867
1065
- - type: ndcg_at_1
1066
- value: 0.79088
1067
- - type: ndcg_at_10
1068
- value: 0.85528
1069
- - type: ndcg_at_100
1070
- value: 0.86134
1071
- - type: ndcg_at_1000
1072
- value: 0.86367
1073
- - type: ndcg_at_3
1074
- value: 0.83943
1075
- - type: ndcg_at_5
1076
- value: 0.84878
1077
- - type: precision_at_1
1078
- value: 0.79088
1079
- - type: precision_at_10
1080
- value: 0.10132
1081
- - type: precision_at_100
1082
- value: 0.01055
1083
- - type: precision_at_1000
1084
- value: 0.00109
1085
- - type: precision_at_3
1086
- value: 0.31963
1087
- - type: precision_at_5
1088
- value: 0.19769
1089
- - type: recall_at_1
1090
- value: 0.73211
1091
- - type: recall_at_10
1092
- value: 0.92797
1093
- - type: recall_at_100
1094
- value: 0.95263
1095
- - type: recall_at_1000
1096
- value: 0.96738
1097
- - type: recall_at_3
1098
- value: 0.88328
1099
- - type: recall_at_5
1100
- value: 0.90821
1101
- - dataset:
1102
- type: mteb/fiqa
1103
- name: MTEB FiQA2018
1104
- config: default
1105
- split: test
1106
- task:
1107
- type: Retrieval
1108
- metrics:
1109
- - type: map_at_1
1110
- value: 0.18311
1111
- - type: map_at_10
1112
- value: 0.29201
1113
- - type: map_at_100
1114
- value: 0.3093
1115
- - type: map_at_1000
1116
- value: 0.31116
1117
- - type: map_at_3
1118
- value: 0.24778
1119
- - type: map_at_5
1120
- value: 0.27453
1121
- - type: mrr_at_1
1122
- value: 0.35494
1123
- - type: mrr_at_10
1124
- value: 0.44489
1125
- - type: mrr_at_100
1126
- value: 0.4532
1127
- - type: mrr_at_1000
1128
- value: 0.45369
1129
- - type: mrr_at_3
1130
- value: 0.41667
1131
- - type: mrr_at_5
1132
- value: 0.43418
1133
- - type: ndcg_at_1
1134
- value: 0.35494
1135
- - type: ndcg_at_10
1136
- value: 0.36868
1137
- - type: ndcg_at_100
1138
- value: 0.43463
1139
- - type: ndcg_at_1000
1140
- value: 0.46766
1141
- - type: ndcg_at_3
1142
- value: 0.32305
1143
- - type: ndcg_at_5
1144
- value: 0.34332
1145
- - type: precision_at_1
1146
- value: 0.35494
1147
- - type: precision_at_10
1148
- value: 0.10324
1149
- - type: precision_at_100
1150
- value: 0.01707
1151
- - type: precision_at_1000
1152
- value: 0.00229
1153
- - type: precision_at_3
1154
- value: 0.21142
1155
- - type: precision_at_5
1156
- value: 0.16327
1157
- - type: recall_at_1
1158
- value: 0.18311
1159
- - type: recall_at_10
1160
- value: 0.43881
1161
- - type: recall_at_100
1162
- value: 0.68593
1163
- - type: recall_at_1000
1164
- value: 0.8855
1165
- - type: recall_at_3
1166
- value: 0.28824
1167
- - type: recall_at_5
1168
- value: 0.36178
1169
- - dataset:
1170
- type: mteb/hotpotqa
1171
- name: MTEB HotpotQA
1172
- config: default
1173
- split: test
1174
- task:
1175
- type: Retrieval
1176
- metrics:
1177
- - type: map_at_1
1178
- value: 0.36766
1179
- - type: map_at_10
1180
- value: 0.53639
1181
- - type: map_at_100
1182
- value: 0.54532
1183
- - type: map_at_1000
1184
- value: 0.54608
1185
- - type: map_at_3
1186
- value: 0.50427
1187
- - type: map_at_5
1188
- value: 0.5245
1189
- - type: mrr_at_1
1190
- value: 0.73531
1191
- - type: mrr_at_10
1192
- value: 0.80104
1193
- - type: mrr_at_100
1194
- value: 0.80341
1195
- - type: mrr_at_1000
1196
- value: 0.80351
1197
- - type: mrr_at_3
1198
- value: 0.78949
1199
- - type: mrr_at_5
1200
- value: 0.79729
1201
- - type: ndcg_at_1
1202
- value: 0.73531
1203
- - type: ndcg_at_10
1204
- value: 0.62918
1205
- - type: ndcg_at_100
1206
- value: 0.66056
1207
- - type: ndcg_at_1000
1208
- value: 0.67554
1209
- - type: ndcg_at_3
1210
- value: 0.58247
1211
- - type: ndcg_at_5
1212
- value: 0.60905
1213
- - type: precision_at_1
1214
- value: 0.73531
1215
- - type: precision_at_10
1216
- value: 0.1302
1217
- - type: precision_at_100
1218
- value: 0.01546
1219
- - type: precision_at_1000
1220
- value: 0.00175
1221
- - type: precision_at_3
1222
- value: 0.36556
1223
- - type: precision_at_5
1224
- value: 0.24032
1225
- - type: recall_at_1
1226
- value: 0.36766
1227
- - type: recall_at_10
1228
- value: 0.65098
1229
- - type: recall_at_100
1230
- value: 0.77306
1231
- - type: recall_at_1000
1232
- value: 0.87252
1233
- - type: recall_at_3
1234
- value: 0.54835
1235
- - type: recall_at_5
1236
- value: 0.60081
1237
- - dataset:
1238
- type: mteb/msmarco
1239
- name: MTEB MSMARCO
1240
- config: default
1241
- split: dev
1242
- task:
1243
- type: Retrieval
1244
- metrics:
1245
- - type: map_at_1
1246
- value: 0.14654
1247
- - type: map_at_10
1248
- value: 0.2472
1249
- - type: map_at_100
1250
- value: 0.25994
1251
- - type: map_at_1000
1252
- value: 0.26067
1253
- - type: map_at_3
1254
- value: 0.21234
1255
- - type: map_at_5
1256
- value: 0.2319
1257
- - type: mrr_at_1
1258
- value: 0.15086
1259
- - type: mrr_at_10
1260
- value: 0.25184
1261
- - type: mrr_at_100
1262
- value: 0.26422
1263
- - type: mrr_at_1000
1264
- value: 0.26489
1265
- - type: mrr_at_3
1266
- value: 0.21731
1267
- - type: mrr_at_5
1268
- value: 0.23674
1269
- - type: ndcg_at_1
1270
- value: 0.15086
1271
- - type: ndcg_at_10
1272
- value: 0.30711
1273
- - type: ndcg_at_100
1274
- value: 0.37221
1275
- - type: ndcg_at_1000
1276
- value: 0.39133
1277
- - type: ndcg_at_3
1278
- value: 0.23567
1279
- - type: ndcg_at_5
1280
- value: 0.27066
1281
- - type: precision_at_1
1282
- value: 0.15086
1283
- - type: precision_at_10
1284
- value: 0.05132
1285
- - type: precision_at_100
1286
- value: 0.00845
1287
- - type: precision_at_1000
1288
- value: 0.00101
1289
- - type: precision_at_3
1290
- value: 0.10277
1291
- - type: precision_at_5
1292
- value: 0.07923
1293
- - type: recall_at_1
1294
- value: 0.14654
1295
- - type: recall_at_10
1296
- value: 0.49341
1297
- - type: recall_at_100
1298
- value: 0.80224
1299
- - type: recall_at_1000
1300
- value: 0.95037
1301
- - type: recall_at_3
1302
- value: 0.29862
1303
- - type: recall_at_5
1304
- value: 0.38274
1305
- - dataset:
1306
- type: mteb/nfcorpus
1307
- name: MTEB NFCorpus
1308
- config: default
1309
- split: test
1310
- task:
1311
- type: Retrieval
1312
- metrics:
1313
- - type: map_at_1
1314
- value: 0.05452
1315
- - type: map_at_10
1316
- value: 0.12758
1317
- - type: map_at_100
1318
- value: 0.1593
1319
- - type: map_at_1000
1320
- value: 0.17422
1321
- - type: map_at_3
1322
- value: 0.0945
1323
- - type: map_at_5
1324
- value: 0.1092
1325
- - type: mrr_at_1
1326
- value: 0.43963
1327
- - type: mrr_at_10
1328
- value: 0.53237
1329
- - type: mrr_at_100
1330
- value: 0.53777
1331
- - type: mrr_at_1000
1332
- value: 0.53822
1333
- - type: mrr_at_3
1334
- value: 0.51445
1335
- - type: mrr_at_5
1336
- value: 0.52466
1337
- - type: ndcg_at_1
1338
- value: 0.41486
1339
- - type: ndcg_at_10
1340
- value: 0.33737
1341
- - type: ndcg_at_100
1342
- value: 0.30886
1343
- - type: ndcg_at_1000
1344
- value: 0.40018
1345
- - type: ndcg_at_3
1346
- value: 0.39324
1347
- - type: ndcg_at_5
1348
- value: 0.36949
1349
- - type: precision_at_1
1350
- value: 0.43344
1351
- - type: precision_at_10
1352
- value: 0.24799
1353
- - type: precision_at_100
1354
- value: 0.07895
1355
- - type: precision_at_1000
1356
- value: 0.02091
1357
- - type: precision_at_3
1358
- value: 0.37152
1359
- - type: precision_at_5
1360
- value: 0.31703
1361
- - type: recall_at_1
1362
- value: 0.05452
1363
- - type: recall_at_10
1364
- value: 0.1712
1365
- - type: recall_at_100
1366
- value: 0.30719
1367
- - type: recall_at_1000
1368
- value: 0.62766
1369
- - type: recall_at_3
1370
- value: 0.10733
1371
- - type: recall_at_5
1372
- value: 0.13553
1373
- - dataset:
1374
- type: mteb/nq
1375
- name: MTEB NQ
1376
- config: default
1377
- split: test
1378
- task:
1379
- type: Retrieval
1380
- metrics:
1381
- - type: map_at_1
1382
- value: 0.29022
1383
- - type: map_at_10
1384
- value: 0.4373
1385
- - type: map_at_100
1386
- value: 0.44849
1387
- - type: map_at_1000
1388
- value: 0.44877
1389
- - type: map_at_3
1390
- value: 0.39045
1391
- - type: map_at_5
1392
- value: 0.4186
1393
- - type: mrr_at_1
1394
- value: 0.32793
1395
- - type: mrr_at_10
1396
- value: 0.46243
1397
- - type: mrr_at_100
1398
- value: 0.47083
1399
- - type: mrr_at_1000
1400
- value: 0.47101
1401
- - type: mrr_at_3
1402
- value: 0.42261
1403
- - type: mrr_at_5
1404
- value: 0.44775
1405
- - type: ndcg_at_1
1406
- value: 0.32793
1407
- - type: ndcg_at_10
1408
- value: 0.51631
1409
- - type: ndcg_at_100
1410
- value: 0.56287
1411
- - type: ndcg_at_1000
1412
- value: 0.56949
1413
- - type: ndcg_at_3
1414
- value: 0.42782
1415
- - type: ndcg_at_5
1416
- value: 0.47554
1417
- - type: precision_at_1
1418
- value: 0.32793
1419
- - type: precision_at_10
1420
- value: 0.08737
1421
- - type: precision_at_100
1422
- value: 0.01134
1423
- - type: precision_at_1000
1424
- value: 0.0012
1425
- - type: precision_at_3
1426
- value: 0.19583
1427
- - type: precision_at_5
1428
- value: 0.14484
1429
- - type: recall_at_1
1430
- value: 0.29022
1431
- - type: recall_at_10
1432
- value: 0.73325
1433
- - type: recall_at_100
1434
- value: 0.93455
1435
- - type: recall_at_1000
1436
- value: 0.98414
1437
- - type: recall_at_3
1438
- value: 0.50406
1439
- - type: recall_at_5
1440
- value: 0.6145
1441
- - dataset:
1442
- type: mteb/quora
1443
- name: MTEB QuoraRetrieval
1444
- config: default
1445
- split: test
1446
- task:
1447
- type: Retrieval
1448
- metrics:
1449
- - type: map_at_1
1450
- value: 0.68941
1451
- - type: map_at_10
1452
- value: 0.82641
1453
- - type: map_at_100
1454
- value: 0.83317
1455
- - type: map_at_1000
1456
- value: 0.83337
1457
- - type: map_at_3
1458
- value: 0.79604
1459
- - type: map_at_5
1460
- value: 0.81525
1461
- - type: mrr_at_1
1462
- value: 0.7935
1463
- - type: mrr_at_10
1464
- value: 0.85969
1465
- - type: mrr_at_100
1466
- value: 0.86094
1467
- - type: mrr_at_1000
1468
- value: 0.86095
1469
- - type: mrr_at_3
1470
- value: 0.84852
1471
- - type: mrr_at_5
1472
- value: 0.85627
1473
- - type: ndcg_at_1
1474
- value: 0.7936
1475
- - type: ndcg_at_10
1476
- value: 0.86687
1477
- - type: ndcg_at_100
1478
- value: 0.88094
1479
- - type: ndcg_at_1000
1480
- value: 0.88243
1481
- - type: ndcg_at_3
1482
- value: 0.83538
1483
- - type: ndcg_at_5
1484
- value: 0.85308
1485
- - type: precision_at_1
1486
- value: 0.7936
1487
- - type: precision_at_10
1488
- value: 0.13145
1489
- - type: precision_at_100
1490
- value: 0.01517
1491
- - type: precision_at_1000
1492
- value: 0.00156
1493
- - type: precision_at_3
1494
- value: 0.36353
1495
- - type: precision_at_5
1496
- value: 0.24044
1497
- - type: recall_at_1
1498
- value: 0.68941
1499
- - type: recall_at_10
1500
- value: 0.94407
1501
- - type: recall_at_100
1502
- value: 0.99226
1503
- - type: recall_at_1000
1504
- value: 0.99958
1505
- - type: recall_at_3
1506
- value: 0.85502
1507
- - type: recall_at_5
1508
- value: 0.90372
1509
- - dataset:
1510
- type: mteb/scidocs
1511
- name: MTEB SCIDOCS
1512
- config: default
1513
- split: test
1514
- task:
1515
- type: Retrieval
1516
- metrics:
1517
- - type: map_at_1
1518
- value: 0.04988
1519
- - type: map_at_10
1520
- value: 0.13553
1521
- - type: map_at_100
1522
- value: 0.16136
1523
- - type: map_at_1000
1524
- value: 0.16512
1525
- - type: map_at_3
1526
- value: 0.09439
1527
- - type: map_at_5
1528
- value: 0.1146
1529
- - type: mrr_at_1
1530
- value: 0.246
1531
- - type: mrr_at_10
1532
- value: 0.36792
1533
- - type: mrr_at_100
1534
- value: 0.37973
1535
- - type: mrr_at_1000
1536
- value: 0.38011
1537
- - type: mrr_at_3
1538
- value: 0.33117
1539
- - type: mrr_at_5
1540
- value: 0.35172
1541
- - type: ndcg_at_1
1542
- value: 0.246
1543
- - type: ndcg_at_10
1544
- value: 0.22542
1545
- - type: ndcg_at_100
1546
- value: 0.32326
1547
- - type: ndcg_at_1000
1548
- value: 0.3828
1549
- - type: ndcg_at_3
1550
- value: 0.20896
1551
- - type: ndcg_at_5
1552
- value: 0.18497
1553
- - type: precision_at_1
1554
- value: 0.246
1555
- - type: precision_at_10
1556
- value: 0.1194
1557
- - type: precision_at_100
1558
- value: 0.02616
1559
- - type: precision_at_1000
1560
- value: 0.00404
1561
- - type: precision_at_3
1562
- value: 0.198
1563
- - type: precision_at_5
1564
- value: 0.1654
1565
- - type: recall_at_1
1566
- value: 0.04988
1567
- - type: recall_at_10
1568
- value: 0.24212
1569
- - type: recall_at_100
1570
- value: 0.53105
1571
- - type: recall_at_1000
1572
- value: 0.82022
1573
- - type: recall_at_3
1574
- value: 0.12047
1575
- - type: recall_at_5
1576
- value: 0.16777
1577
- - dataset:
1578
- type: mteb/scifact
1579
- name: MTEB SciFact
1580
- config: default
1581
- split: test
1582
- task:
1583
- type: Retrieval
1584
- metrics:
1585
- - type: map_at_1
1586
- value: 0.56578
1587
- - type: map_at_10
1588
- value: 0.66725
1589
- - type: map_at_100
1590
- value: 0.67379
1591
- - type: map_at_1000
1592
- value: 0.674
1593
- - type: map_at_3
1594
- value: 0.63416
1595
- - type: map_at_5
1596
- value: 0.6577
1597
- - type: mrr_at_1
1598
- value: 0.59333
1599
- - type: mrr_at_10
1600
- value: 0.67533
1601
- - type: mrr_at_100
1602
- value: 0.68062
1603
- - type: mrr_at_1000
1604
- value: 0.68082
1605
- - type: mrr_at_3
1606
- value: 0.64944
1607
- - type: mrr_at_5
1608
- value: 0.66928
1609
- - type: ndcg_at_1
1610
- value: 0.59333
1611
- - type: ndcg_at_10
1612
- value: 0.7127
1613
- - type: ndcg_at_100
1614
- value: 0.73889
1615
- - type: ndcg_at_1000
1616
- value: 0.7441
1617
- - type: ndcg_at_3
1618
- value: 0.65793
1619
- - type: ndcg_at_5
1620
- value: 0.69429
1621
- - type: precision_at_1
1622
- value: 0.59333
1623
- - type: precision_at_10
1624
- value: 0.096
1625
- - type: precision_at_100
1626
- value: 0.01087
1627
- - type: precision_at_1000
1628
- value: 0.00113
1629
- - type: precision_at_3
1630
- value: 0.25556
1631
- - type: precision_at_5
1632
- value: 0.17667
1633
- - type: recall_at_1
1634
- value: 0.56578
1635
- - type: recall_at_10
1636
- value: 0.842
1637
- - type: recall_at_100
1638
- value: 0.95667
1639
- - type: recall_at_1000
1640
- value: 0.99667
1641
- - type: recall_at_3
1642
- value: 0.70072
1643
- - type: recall_at_5
1644
- value: 0.79011
1645
- - dataset:
1646
- type: mteb/touche2020
1647
- name: MTEB Touche2020
1648
- config: default
1649
- split: test
1650
- task:
1651
- type: Retrieval
1652
- metrics:
1653
- - type: map_at_1
1654
- value: 0.01976
1655
- - type: map_at_10
1656
- value: 0.09688
1657
- - type: map_at_100
1658
- value: 0.15117
1659
- - type: map_at_1000
1660
- value: 0.16769
1661
- - type: map_at_3
1662
- value: 0.04589
1663
- - type: map_at_5
1664
- value: 0.06556
1665
- - type: mrr_at_1
1666
- value: 0.26531
1667
- - type: mrr_at_10
1668
- value: 0.43863
1669
- - type: mrr_at_100
1670
- value: 0.44767
1671
- - type: mrr_at_1000
1672
- value: 0.44767
1673
- - type: mrr_at_3
1674
- value: 0.39116
1675
- - type: mrr_at_5
1676
- value: 0.41156
1677
- - type: ndcg_at_1
1678
- value: 0.23469
1679
- - type: ndcg_at_10
1680
- value: 0.24029
1681
- - type: ndcg_at_100
1682
- value: 0.34425
1683
- - type: ndcg_at_1000
1684
- value: 0.46907
1685
- - type: ndcg_at_3
1686
- value: 0.25522
1687
- - type: ndcg_at_5
1688
- value: 0.24333
1689
- - type: precision_at_1
1690
- value: 0.26531
1691
- - type: precision_at_10
1692
- value: 0.22449
1693
- - type: precision_at_100
1694
- value: 0.07122
1695
- - type: precision_at_1000
1696
- value: 0.01527
1697
- - type: precision_at_3
1698
- value: 0.27891
1699
- - type: precision_at_5
1700
- value: 0.25714
1701
- - type: recall_at_1
1702
- value: 0.01976
1703
- - type: recall_at_10
1704
- value: 0.16633
1705
- - type: recall_at_100
1706
- value: 0.4561
1707
- - type: recall_at_1000
1708
- value: 0.82481
1709
- - type: recall_at_3
1710
- value: 0.06101
1711
- - type: recall_at_5
1712
- value: 0.0968
1713
- - dataset:
1714
- type: mteb/trec-covid
1715
- name: MTEB TRECCOVID
1716
- config: default
1717
- split: test
1718
- task:
1719
- type: Retrieval
1720
- metrics:
1721
- - type: map_at_1
1722
- value: 0.00211
1723
- - type: map_at_10
1724
- value: 0.01526
1725
- - type: map_at_100
1726
- value: 0.08863
1727
- - type: map_at_1000
1728
- value: 0.23162
1729
- - type: map_at_3
1730
- value: 0.00555
1731
- - type: map_at_5
1732
- value: 0.00873
1733
- - type: mrr_at_1
1734
- value: 0.76
1735
- - type: mrr_at_10
1736
- value: 0.8485
1737
- - type: mrr_at_100
1738
- value: 0.8485
1739
- - type: mrr_at_1000
1740
- value: 0.8485
1741
- - type: mrr_at_3
1742
- value: 0.84
1743
- - type: mrr_at_5
1744
- value: 0.844
1745
- - type: ndcg_at_1
1746
- value: 0.7
1747
- - type: ndcg_at_10
1748
- value: 0.63098
1749
- - type: ndcg_at_100
1750
- value: 0.49847
1751
- - type: ndcg_at_1000
1752
- value: 0.48395
1753
- - type: ndcg_at_3
1754
- value: 0.68704
1755
- - type: ndcg_at_5
1756
- value: 0.67533
1757
- - type: precision_at_1
1758
- value: 0.76
1759
- - type: precision_at_10
1760
- value: 0.66
1761
- - type: precision_at_100
1762
- value: 0.5134
1763
- - type: precision_at_1000
1764
- value: 0.2168
1765
- - type: precision_at_3
1766
- value: 0.72667
1767
- - type: precision_at_5
1768
- value: 0.716
1769
- - type: recall_at_1
1770
- value: 0.00211
1771
- - type: recall_at_10
1772
- value: 0.01748
1773
- - type: recall_at_100
1774
- value: 0.12448
1775
- - type: recall_at_1000
1776
- value: 0.46795
1777
- - type: recall_at_3
1778
- value: 0.00593
1779
- - type: recall_at_5
1780
- value: 0.00962
1781
- pipeline_tag: sentence-similarity
1782
- ---
1783
  # Granite-Embedding-30m-English
1784
 
1785
  **Model Summary:**
1786
- Granite-Embedding-30m-English is a 30M parameter model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. This model is developed using retrieval oriented pretraining, contrastive finetuning, knowledge distillation and model merging for improved performance.
1787
 
1788
  - **Developers:** Granite Embedding Team, IBM
1789
- - **GitHub Repository:**
1790
  - **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
1791
- - **Paper:**
1792
  - **Release Date**: December 18th, 2024
1793
  - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
1794
 
@@ -1875,11 +1874,11 @@ query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
1875
  ```
1876
  **Evaluation:**
1877
 
1878
- Granite-Embedding-30M-English is twice as fast as other models with similar embedding dimensions, while maintaining competitive performance. The performance of the Granite-Embedding-30M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below. MTEB Retrieval(14) indicates the average BEIR performance excluding MS-MARCO task as, unlike all other models, Granite-Embedding-30M-English did not train on MS-MARCO due to the dataset's non-commercial license. The average time required to encode and retrieve per query is also reported.
1879
 
1880
- | Model | Paramters (M)| Embedding Dimension | MTEB Retrieval (15) | MTEB Retrieval (14) | CoIR (10) | Retrieval Time (seconds/query)|
1881
- |---------------------------------|-------------:|--------------------:|--------------------:|---------------------:|----------:|------------------------------:|
1882
- |granite-embedding-30m-english |30 |384 |49.1 |50.4 |47.0 | 0.16 |
1883
 
1884
 
1885
  **Model Architecture:**
 
1
+ ---
2
+ language:
3
+ - en
4
+ license: apache-2.0
5
+ library_name: transformers
6
+ tags:
7
+ - language
8
+ - granite
9
+ - embeddings
10
+ model-index:
11
+ - name: ibm-granite/granite-embedding-30m-english
12
+ results:
13
+ - dataset:
14
+ type: mteb/arguana
15
+ name: MTEB ArguaAna
16
+ config: default
17
+ split: test
18
+ task:
19
+ type: Retrieval
20
+ metrics:
21
+ - type: map_at_1
22
+ value: 0.31792
23
+ - type: map_at_10
24
+ value: 0.47599
25
+ - type: map_at_100
26
+ value: 0.48425
27
+ - type: map_at_1000
28
+ value: 0.48427
29
+ - type: map_at_3
30
+ value: 0.42757
31
+ - type: map_at_5
32
+ value: 0.45634
33
+ - type: mrr_at_1
34
+ value: 0.32788
35
+ - type: mrr_at_10
36
+ value: 0.47974
37
+ - type: mrr_at_100
38
+ value: 0.48801
39
+ - type: mrr_at_1000
40
+ value: 0.48802
41
+ - type: mrr_at_3
42
+ value: 0.43065
43
+ - type: mrr_at_5
44
+ value: 0.45999
45
+ - type: ndcg_at_1
46
+ value: 0.31792
47
+ - type: ndcg_at_10
48
+ value: 0.56356
49
+ - type: ndcg_at_100
50
+ value: 0.59789
51
+ - type: ndcg_at_1000
52
+ value: 0.59857
53
+ - type: ndcg_at_3
54
+ value: 0.46453
55
+ - type: ndcg_at_5
56
+ value: 0.51623
57
+ - type: precision_at_1
58
+ value: 0.31792
59
+ - type: precision_at_10
60
+ value: 0.08428
61
+ - type: precision_at_100
62
+ value: 0.00991
63
+ - type: precision_at_1000
64
+ value: 0.001
65
+ - type: precision_at_3
66
+ value: 0.19061
67
+ - type: precision_at_5
68
+ value: 0.1394
69
+ - type: recall_at_1
70
+ value: 0.31792
71
+ - type: recall_at_10
72
+ value: 0.84282
73
+ - type: recall_at_100
74
+ value: 0.99075
75
+ - type: recall_at_1000
76
+ value: 0.99644
77
+ - type: recall_at_3
78
+ value: 0.57183
79
+ - type: recall_at_5
80
+ value: 0.69701
81
+ - dataset:
82
+ type: mteb/climate-fever
83
+ name: MTEB ClimateFEVER
84
+ config: default
85
+ split: test
86
+ task:
87
+ type: Retrieval
88
+ metrics:
89
+ - type: map_at_1
90
+ value: 0.13189
91
+ - type: map_at_10
92
+ value: 0.21789
93
+ - type: map_at_100
94
+ value: 0.2358
95
+ - type: map_at_1000
96
+ value: 0.23772
97
+ - type: map_at_3
98
+ value: 0.18513
99
+ - type: map_at_5
100
+ value: 0.20212
101
+ - type: mrr_at_1
102
+ value: 0.29837
103
+ - type: mrr_at_10
104
+ value: 0.41376
105
+ - type: mrr_at_100
106
+ value: 0.42282
107
+ - type: mrr_at_1000
108
+ value: 0.42319
109
+ - type: mrr_at_3
110
+ value: 0.38284
111
+ - type: mrr_at_5
112
+ value: 0.40301
113
+ - type: ndcg_at_1
114
+ value: 0.29837
115
+ - type: ndcg_at_10
116
+ value: 0.30263
117
+ - type: ndcg_at_100
118
+ value: 0.37228
119
+ - type: ndcg_at_1000
120
+ value: 0.40677
121
+ - type: ndcg_at_3
122
+ value: 0.25392
123
+ - type: ndcg_at_5
124
+ value: 0.27153
125
+ - type: precision_at_1
126
+ value: 0.29837
127
+ - type: precision_at_10
128
+ value: 0.09179
129
+ - type: precision_at_100
130
+ value: 0.01659
131
+ - type: precision_at_1000
132
+ value: 0.0023
133
+ - type: precision_at_3
134
+ value: 0.18545
135
+ - type: precision_at_5
136
+ value: 0.14241
137
+ - type: recall_at_1
138
+ value: 0.13189
139
+ - type: recall_at_10
140
+ value: 0.35355
141
+ - type: recall_at_100
142
+ value: 0.59255
143
+ - type: recall_at_1000
144
+ value: 0.78637
145
+ - type: recall_at_3
146
+ value: 0.23255
147
+ - type: recall_at_5
148
+ value: 0.28446
149
+ - dataset:
150
+ type: mteb/cqadupstack-android
151
+ name: MTEB CQADupstackAndroidRetrieval
152
+ config: default
153
+ split: test
154
+ task:
155
+ type: Retrieval
156
+ metrics:
157
+ - type: map_at_1
158
+ value: 0.35797
159
+ - type: map_at_10
160
+ value: 0.47793
161
+ - type: map_at_100
162
+ value: 0.49422
163
+ - type: map_at_1000
164
+ value: 0.49546
165
+ - type: map_at_3
166
+ value: 0.44137
167
+ - type: map_at_5
168
+ value: 0.46063
169
+ - type: mrr_at_1
170
+ value: 0.44206
171
+ - type: mrr_at_10
172
+ value: 0.53808
173
+ - type: mrr_at_100
174
+ value: 0.5454
175
+ - type: mrr_at_1000
176
+ value: 0.54578
177
+ - type: mrr_at_3
178
+ value: 0.51431
179
+ - type: mrr_at_5
180
+ value: 0.5284
181
+ - type: ndcg_at_1
182
+ value: 0.44206
183
+ - type: ndcg_at_10
184
+ value: 0.54106
185
+ - type: ndcg_at_100
186
+ value: 0.59335
187
+ - type: ndcg_at_1000
188
+ value: 0.61015
189
+ - type: ndcg_at_3
190
+ value: 0.49365
191
+ - type: ndcg_at_5
192
+ value: 0.51429
193
+ - type: precision_at_1
194
+ value: 0.44206
195
+ - type: precision_at_10
196
+ value: 0.10443
197
+ - type: precision_at_100
198
+ value: 0.01631
199
+ - type: precision_at_1000
200
+ value: 0.00214
201
+ - type: precision_at_3
202
+ value: 0.23653
203
+ - type: precision_at_5
204
+ value: 0.1691
205
+ - type: recall_at_1
206
+ value: 0.35797
207
+ - type: recall_at_10
208
+ value: 0.65182
209
+ - type: recall_at_100
210
+ value: 0.86654
211
+ - type: recall_at_1000
212
+ value: 0.97131
213
+ - type: recall_at_3
214
+ value: 0.51224
215
+ - type: recall_at_5
216
+ value: 0.57219
217
+ - dataset:
218
+ type: mteb/cqadupstack-english
219
+ name: MTEB CQADupstackEnglishRetrieval
220
+ config: default
221
+ split: test
222
+ task:
223
+ type: Retrieval
224
+ metrics:
225
+ - type: map_at_1
226
+ value: 0.32748
227
+ - type: map_at_10
228
+ value: 0.44138
229
+ - type: map_at_100
230
+ value: 0.45565
231
+ - type: map_at_1000
232
+ value: 0.45698
233
+ - type: map_at_3
234
+ value: 0.40916
235
+ - type: map_at_5
236
+ value: 0.42621
237
+ - type: mrr_at_1
238
+ value: 0.41274
239
+ - type: mrr_at_10
240
+ value: 0.5046
241
+ - type: mrr_at_100
242
+ value: 0.5107
243
+ - type: mrr_at_1000
244
+ value: 0.51109
245
+ - type: mrr_at_3
246
+ value: 0.48238
247
+ - type: mrr_at_5
248
+ value: 0.49563
249
+ - type: ndcg_at_1
250
+ value: 0.41274
251
+ - type: ndcg_at_10
252
+ value: 0.50251
253
+ - type: ndcg_at_100
254
+ value: 0.54725
255
+ - type: ndcg_at_1000
256
+ value: 0.56635
257
+ - type: ndcg_at_3
258
+ value: 0.46023
259
+ - type: ndcg_at_5
260
+ value: 0.47883
261
+ - type: precision_at_1
262
+ value: 0.41274
263
+ - type: precision_at_10
264
+ value: 0.09828
265
+ - type: precision_at_100
266
+ value: 0.01573
267
+ - type: precision_at_1000
268
+ value: 0.00202
269
+ - type: precision_at_3
270
+ value: 0.22718
271
+ - type: precision_at_5
272
+ value: 0.16064
273
+ - type: recall_at_1
274
+ value: 0.32748
275
+ - type: recall_at_10
276
+ value: 0.60322
277
+ - type: recall_at_100
278
+ value: 0.79669
279
+ - type: recall_at_1000
280
+ value: 0.9173
281
+ - type: recall_at_3
282
+ value: 0.47523
283
+ - type: recall_at_5
284
+ value: 0.52957
285
+ - dataset:
286
+ type: mteb/cqadupstack-gaming
287
+ name: MTEB CQADupstackGamingRetrieval
288
+ config: default
289
+ split: test
290
+ task:
291
+ type: Retrieval
292
+ metrics:
293
+ - type: map_at_1
294
+ value: 0.41126
295
+ - type: map_at_10
296
+ value: 0.53661
297
+ - type: map_at_100
298
+ value: 0.54588
299
+ - type: map_at_1000
300
+ value: 0.54638
301
+ - type: map_at_3
302
+ value: 0.50389
303
+ - type: map_at_5
304
+ value: 0.52286
305
+ - type: mrr_at_1
306
+ value: 0.47147
307
+ - type: mrr_at_10
308
+ value: 0.5685
309
+ - type: mrr_at_100
310
+ value: 0.57458
311
+ - type: mrr_at_1000
312
+ value: 0.57487
313
+ - type: mrr_at_3
314
+ value: 0.54431
315
+ - type: mrr_at_5
316
+ value: 0.55957
317
+ - type: ndcg_at_1
318
+ value: 0.47147
319
+ - type: ndcg_at_10
320
+ value: 0.59318
321
+ - type: ndcg_at_100
322
+ value: 0.62972
323
+ - type: ndcg_at_1000
324
+ value: 0.64033
325
+ - type: ndcg_at_3
326
+ value: 0.53969
327
+ - type: ndcg_at_5
328
+ value: 0.56743
329
+ - type: precision_at_1
330
+ value: 0.47147
331
+ - type: precision_at_10
332
+ value: 0.09549
333
+ - type: precision_at_100
334
+ value: 0.01224
335
+ - type: precision_at_1000
336
+ value: 0.00135
337
+ - type: precision_at_3
338
+ value: 0.24159
339
+ - type: precision_at_5
340
+ value: 0.16577
341
+ - type: recall_at_1
342
+ value: 0.41126
343
+ - type: recall_at_10
344
+ value: 0.72691
345
+ - type: recall_at_100
346
+ value: 0.88692
347
+ - type: recall_at_1000
348
+ value: 0.96232
349
+ - type: recall_at_3
350
+ value: 0.58374
351
+ - type: recall_at_5
352
+ value: 0.65226
353
+ - dataset:
354
+ type: mteb/cqadupstack-gis
355
+ name: MTEB CQADupstackGisRetrieval
356
+ config: default
357
+ split: test
358
+ task:
359
+ type: Retrieval
360
+ metrics:
361
+ - type: map_at_1
362
+ value: 0.28464
363
+ - type: map_at_10
364
+ value: 0.3828
365
+ - type: map_at_100
366
+ value: 0.39277
367
+ - type: map_at_1000
368
+ value: 0.39355
369
+ - type: map_at_3
370
+ value: 0.35704
371
+ - type: map_at_5
372
+ value: 0.37116
373
+ - type: mrr_at_1
374
+ value: 0.30734
375
+ - type: mrr_at_10
376
+ value: 0.40422
377
+ - type: mrr_at_100
378
+ value: 0.41297
379
+ - type: mrr_at_1000
380
+ value: 0.41355
381
+ - type: mrr_at_3
382
+ value: 0.38136
383
+ - type: mrr_at_5
384
+ value: 0.39362
385
+ - type: ndcg_at_1
386
+ value: 0.30734
387
+ - type: ndcg_at_10
388
+ value: 0.43564
389
+ - type: ndcg_at_100
390
+ value: 0.48419
391
+ - type: ndcg_at_1000
392
+ value: 0.50404
393
+ - type: ndcg_at_3
394
+ value: 0.38672
395
+ - type: ndcg_at_5
396
+ value: 0.40954
397
+ - type: precision_at_1
398
+ value: 0.30734
399
+ - type: precision_at_10
400
+ value: 0.06633
401
+ - type: precision_at_100
402
+ value: 0.00956
403
+ - type: precision_at_1000
404
+ value: 0.00116
405
+ - type: precision_at_3
406
+ value: 0.16497
407
+ - type: precision_at_5
408
+ value: 0.11254
409
+ - type: recall_at_1
410
+ value: 0.28464
411
+ - type: recall_at_10
412
+ value: 0.57621
413
+ - type: recall_at_100
414
+ value: 0.7966
415
+ - type: recall_at_1000
416
+ value: 0.94633
417
+ - type: recall_at_3
418
+ value: 0.44588
419
+ - type: recall_at_5
420
+ value: 0.50031
421
+ - dataset:
422
+ type: mteb/cqadupstack-mathematica
423
+ name: MTEB CQADupstackMathematicaRetrieval
424
+ config: default
425
+ split: test
426
+ task:
427
+ type: Retrieval
428
+ metrics:
429
+ - type: map_at_1
430
+ value: 0.18119
431
+ - type: map_at_10
432
+ value: 0.27055
433
+ - type: map_at_100
434
+ value: 0.28461
435
+ - type: map_at_1000
436
+ value: 0.28577
437
+ - type: map_at_3
438
+ value: 0.24341
439
+ - type: map_at_5
440
+ value: 0.25861
441
+ - type: mrr_at_1
442
+ value: 0.22886
443
+ - type: mrr_at_10
444
+ value: 0.32234
445
+ - type: mrr_at_100
446
+ value: 0.3328
447
+ - type: mrr_at_1000
448
+ value: 0.3334
449
+ - type: mrr_at_3
450
+ value: 0.29664
451
+ - type: mrr_at_5
452
+ value: 0.31107
453
+ - type: ndcg_at_1
454
+ value: 0.22886
455
+ - type: ndcg_at_10
456
+ value: 0.32749
457
+ - type: ndcg_at_100
458
+ value: 0.39095
459
+ - type: ndcg_at_1000
460
+ value: 0.41656
461
+ - type: ndcg_at_3
462
+ value: 0.27864
463
+ - type: ndcg_at_5
464
+ value: 0.30177
465
+ - type: precision_at_1
466
+ value: 0.22886
467
+ - type: precision_at_10
468
+ value: 0.06169
469
+ - type: precision_at_100
470
+ value: 0.0107
471
+ - type: precision_at_1000
472
+ value: 0.00143
473
+ - type: precision_at_3
474
+ value: 0.13682
475
+ - type: precision_at_5
476
+ value: 0.0995
477
+ - type: recall_at_1
478
+ value: 0.18119
479
+ - type: recall_at_10
480
+ value: 0.44983
481
+ - type: recall_at_100
482
+ value: 0.72396
483
+ - type: recall_at_1000
484
+ value: 0.90223
485
+ - type: recall_at_3
486
+ value: 0.31633
487
+ - type: recall_at_5
488
+ value: 0.37532
489
+ - dataset:
490
+ type: mteb/cqadupstack-physics
491
+ name: MTEB CQADupstackPhysicsRetrieval
492
+ config: default
493
+ split: test
494
+ task:
495
+ type: Retrieval
496
+ metrics:
497
+ - type: map_at_1
498
+ value: 0.30517
499
+ - type: map_at_10
500
+ value: 0.42031
501
+ - type: map_at_100
502
+ value: 0.43415
503
+ - type: map_at_1000
504
+ value: 0.43525
505
+ - type: map_at_3
506
+ value: 0.38443
507
+ - type: map_at_5
508
+ value: 0.40685
509
+ - type: mrr_at_1
510
+ value: 0.38114
511
+ - type: mrr_at_10
512
+ value: 0.47783
513
+ - type: mrr_at_100
514
+ value: 0.48647
515
+ - type: mrr_at_1000
516
+ value: 0.48688
517
+ - type: mrr_at_3
518
+ value: 0.45172
519
+ - type: mrr_at_5
520
+ value: 0.46817
521
+ - type: ndcg_at_1
522
+ value: 0.38114
523
+ - type: ndcg_at_10
524
+ value: 0.4834
525
+ - type: ndcg_at_100
526
+ value: 0.53861
527
+ - type: ndcg_at_1000
528
+ value: 0.55701
529
+ - type: ndcg_at_3
530
+ value: 0.42986
531
+ - type: ndcg_at_5
532
+ value: 0.45893
533
+ - type: precision_at_1
534
+ value: 0.38114
535
+ - type: precision_at_10
536
+ value: 0.08893
537
+ - type: precision_at_100
538
+ value: 0.01375
539
+ - type: precision_at_1000
540
+ value: 0.00172
541
+ - type: precision_at_3
542
+ value: 0.20821
543
+ - type: precision_at_5
544
+ value: 0.15034
545
+ - type: recall_at_1
546
+ value: 0.30517
547
+ - type: recall_at_10
548
+ value: 0.61332
549
+ - type: recall_at_100
550
+ value: 0.84051
551
+ - type: recall_at_1000
552
+ value: 0.95826
553
+ - type: recall_at_3
554
+ value: 0.46015
555
+ - type: recall_at_5
556
+ value: 0.53801
557
+ - dataset:
558
+ type: mteb/cqadupstack-programmers
559
+ name: MTEB CQADupstackProgrammersRetrieval
560
+ config: default
561
+ split: test
562
+ task:
563
+ type: Retrieval
564
+ metrics:
565
+ - type: map_at_1
566
+ value: 0.27396
567
+ - type: map_at_10
568
+ value: 0.38043
569
+ - type: map_at_100
570
+ value: 0.39341
571
+ - type: map_at_1000
572
+ value: 0.39454
573
+ - type: map_at_3
574
+ value: 0.34783
575
+ - type: map_at_5
576
+ value: 0.3663
577
+ - type: mrr_at_1
578
+ value: 0.34247
579
+ - type: mrr_at_10
580
+ value: 0.43681
581
+ - type: mrr_at_100
582
+ value: 0.4451
583
+ - type: mrr_at_1000
584
+ value: 0.44569
585
+ - type: mrr_at_3
586
+ value: 0.41172
587
+ - type: mrr_at_5
588
+ value: 0.42702
589
+ - type: ndcg_at_1
590
+ value: 0.34247
591
+ - type: ndcg_at_10
592
+ value: 0.44065
593
+ - type: ndcg_at_100
594
+ value: 0.49434
595
+ - type: ndcg_at_1000
596
+ value: 0.51682
597
+ - type: ndcg_at_3
598
+ value: 0.38976
599
+ - type: ndcg_at_5
600
+ value: 0.41332
601
+ - type: precision_at_1
602
+ value: 0.34247
603
+ - type: precision_at_10
604
+ value: 0.08059
605
+ - type: precision_at_100
606
+ value: 0.01258
607
+ - type: precision_at_1000
608
+ value: 0.00162
609
+ - type: precision_at_3
610
+ value: 0.1876
611
+ - type: precision_at_5
612
+ value: 0.13333
613
+ - type: recall_at_1
614
+ value: 0.27396
615
+ - type: recall_at_10
616
+ value: 0.56481
617
+ - type: recall_at_100
618
+ value: 0.79012
619
+ - type: recall_at_1000
620
+ value: 0.94182
621
+ - type: recall_at_3
622
+ value: 0.41785
623
+ - type: recall_at_5
624
+ value: 0.48303
625
+ - dataset:
626
+ type: mteb/cqadupstack-stats
627
+ name: MTEB CQADupstackStatsRetrieval
628
+ config: default
629
+ split: test
630
+ task:
631
+ type: Retrieval
632
+ metrics:
633
+ - type: map_at_1
634
+ value: 0.25728
635
+ - type: map_at_10
636
+ value: 0.33903
637
+ - type: map_at_100
638
+ value: 0.34853
639
+ - type: map_at_1000
640
+ value: 0.34944
641
+ - type: map_at_3
642
+ value: 0.31268
643
+ - type: map_at_5
644
+ value: 0.32596
645
+ - type: mrr_at_1
646
+ value: 0.29141
647
+ - type: mrr_at_10
648
+ value: 0.36739
649
+ - type: mrr_at_100
650
+ value: 0.37545
651
+ - type: mrr_at_1000
652
+ value: 0.37608
653
+ - type: mrr_at_3
654
+ value: 0.34407
655
+ - type: mrr_at_5
656
+ value: 0.3568
657
+ - type: ndcg_at_1
658
+ value: 0.29141
659
+ - type: ndcg_at_10
660
+ value: 0.38596
661
+ - type: ndcg_at_100
662
+ value: 0.43375
663
+ - type: ndcg_at_1000
664
+ value: 0.45562
665
+ - type: ndcg_at_3
666
+ value: 0.33861
667
+ - type: ndcg_at_5
668
+ value: 0.35887
669
+ - type: precision_at_1
670
+ value: 0.29141
671
+ - type: precision_at_10
672
+ value: 0.06334
673
+ - type: precision_at_100
674
+ value: 0.00952
675
+ - type: precision_at_1000
676
+ value: 0.00121
677
+ - type: precision_at_3
678
+ value: 0.14826
679
+ - type: precision_at_5
680
+ value: 0.10429
681
+ - type: recall_at_1
682
+ value: 0.25728
683
+ - type: recall_at_10
684
+ value: 0.50121
685
+ - type: recall_at_100
686
+ value: 0.72382
687
+ - type: recall_at_1000
688
+ value: 0.88306
689
+ - type: recall_at_3
690
+ value: 0.36638
691
+ - type: recall_at_5
692
+ value: 0.41689
693
+ - dataset:
694
+ type: mteb/cqadupstack-tex
695
+ name: MTEB CQADupstackTexRetrieval
696
+ config: default
697
+ split: test
698
+ task:
699
+ type: Retrieval
700
+ metrics:
701
+ - type: map_at_1
702
+ value: 0.19911
703
+ - type: map_at_10
704
+ value: 0.2856
705
+ - type: map_at_100
706
+ value: 0.29785
707
+ - type: map_at_1000
708
+ value: 0.29911
709
+ - type: map_at_3
710
+ value: 0.25875
711
+ - type: map_at_5
712
+ value: 0.2741
713
+ - type: mrr_at_1
714
+ value: 0.24054
715
+ - type: mrr_at_10
716
+ value: 0.32483
717
+ - type: mrr_at_100
718
+ value: 0.33464
719
+ - type: mrr_at_1000
720
+ value: 0.33534
721
+ - type: mrr_at_3
722
+ value: 0.30162
723
+ - type: mrr_at_5
724
+ value: 0.31506
725
+ - type: ndcg_at_1
726
+ value: 0.24054
727
+ - type: ndcg_at_10
728
+ value: 0.33723
729
+ - type: ndcg_at_100
730
+ value: 0.39362
731
+ - type: ndcg_at_1000
732
+ value: 0.42065
733
+ - type: ndcg_at_3
734
+ value: 0.29116
735
+ - type: ndcg_at_5
736
+ value: 0.31299
737
+ - type: precision_at_1
738
+ value: 0.24054
739
+ - type: precision_at_10
740
+ value: 0.06194
741
+ - type: precision_at_100
742
+ value: 0.01058
743
+ - type: precision_at_1000
744
+ value: 0.00148
745
+ - type: precision_at_3
746
+ value: 0.13914
747
+ - type: precision_at_5
748
+ value: 0.10076
749
+ - type: recall_at_1
750
+ value: 0.19911
751
+ - type: recall_at_10
752
+ value: 0.45183
753
+ - type: recall_at_100
754
+ value: 0.7025
755
+ - type: recall_at_1000
756
+ value: 0.89222
757
+ - type: recall_at_3
758
+ value: 0.32195
759
+ - type: recall_at_5
760
+ value: 0.37852
761
+ - dataset:
762
+ type: mteb/cqadupstack-unix
763
+ name: MTEB CQADupstackUnixRetrieval
764
+ config: default
765
+ split: test
766
+ task:
767
+ type: Retrieval
768
+ metrics:
769
+ - type: map_at_1
770
+ value: 0.29819
771
+ - type: map_at_10
772
+ value: 0.40073
773
+ - type: map_at_100
774
+ value: 0.41289
775
+ - type: map_at_1000
776
+ value: 0.41375
777
+ - type: map_at_3
778
+ value: 0.36572
779
+ - type: map_at_5
780
+ value: 0.38386
781
+ - type: mrr_at_1
782
+ value: 0.35168
783
+ - type: mrr_at_10
784
+ value: 0.44381
785
+ - type: mrr_at_100
786
+ value: 0.45191
787
+ - type: mrr_at_1000
788
+ value: 0.45234
789
+ - type: mrr_at_3
790
+ value: 0.41402
791
+ - type: mrr_at_5
792
+ value: 0.43039
793
+ - type: ndcg_at_1
794
+ value: 0.35168
795
+ - type: ndcg_at_10
796
+ value: 0.46071
797
+ - type: ndcg_at_100
798
+ value: 0.51351
799
+ - type: ndcg_at_1000
800
+ value: 0.5317
801
+ - type: ndcg_at_3
802
+ value: 0.39972
803
+ - type: ndcg_at_5
804
+ value: 0.42586
805
+ - type: precision_at_1
806
+ value: 0.35168
807
+ - type: precision_at_10
808
+ value: 0.07985
809
+ - type: precision_at_100
810
+ value: 0.01185
811
+ - type: precision_at_1000
812
+ value: 0.00144
813
+ - type: precision_at_3
814
+ value: 0.18221
815
+ - type: precision_at_5
816
+ value: 0.12892
817
+ - type: recall_at_1
818
+ value: 0.29819
819
+ - type: recall_at_10
820
+ value: 0.60075
821
+ - type: recall_at_100
822
+ value: 0.82771
823
+ - type: recall_at_1000
824
+ value: 0.95219
825
+ - type: recall_at_3
826
+ value: 0.43245
827
+ - type: recall_at_5
828
+ value: 0.49931
829
+ - dataset:
830
+ type: mteb/cqadupstack-webmasters
831
+ name: MTEB CQADupstackWebmastersRetrieval
832
+ config: default
833
+ split: test
834
+ task:
835
+ type: Retrieval
836
+ metrics:
837
+ - type: map_at_1
838
+ value: 0.28409
839
+ - type: map_at_10
840
+ value: 0.37621
841
+ - type: map_at_100
842
+ value: 0.39233
843
+ - type: map_at_1000
844
+ value: 0.39471
845
+ - type: map_at_3
846
+ value: 0.34337
847
+ - type: map_at_5
848
+ value: 0.35985
849
+ - type: mrr_at_1
850
+ value: 0.33794
851
+ - type: mrr_at_10
852
+ value: 0.42349
853
+ - type: mrr_at_100
854
+ value: 0.43196
855
+ - type: mrr_at_1000
856
+ value: 0.43237
857
+ - type: mrr_at_3
858
+ value: 0.39526
859
+ - type: mrr_at_5
860
+ value: 0.41087
861
+ - type: ndcg_at_1
862
+ value: 0.33794
863
+ - type: ndcg_at_10
864
+ value: 0.43832
865
+ - type: ndcg_at_100
866
+ value: 0.49514
867
+ - type: ndcg_at_1000
868
+ value: 0.51742
869
+ - type: ndcg_at_3
870
+ value: 0.38442
871
+ - type: ndcg_at_5
872
+ value: 0.40737
873
+ - type: precision_at_1
874
+ value: 0.33794
875
+ - type: precision_at_10
876
+ value: 0.08597
877
+ - type: precision_at_100
878
+ value: 0.01652
879
+ - type: precision_at_1000
880
+ value: 0.00251
881
+ - type: precision_at_3
882
+ value: 0.17787
883
+ - type: precision_at_5
884
+ value: 0.13241
885
+ - type: recall_at_1
886
+ value: 0.28409
887
+ - type: recall_at_10
888
+ value: 0.55388
889
+ - type: recall_at_100
890
+ value: 0.81517
891
+ - type: recall_at_1000
892
+ value: 0.95038
893
+ - type: recall_at_3
894
+ value: 0.40133
895
+ - type: recall_at_5
896
+ value: 0.45913
897
+ - dataset:
898
+ type: mteb/cqadupstack-wordpress
899
+ name: MTEB CQADupstackWordpressRetrieval
900
+ config: default
901
+ split: test
902
+ task:
903
+ type: Retrieval
904
+ metrics:
905
+ - type: map_at_1
906
+ value: 0.24067
907
+ - type: map_at_10
908
+ value: 0.32184
909
+ - type: map_at_100
910
+ value: 0.33357
911
+ - type: map_at_1000
912
+ value: 0.33458
913
+ - type: map_at_3
914
+ value: 0.29492
915
+ - type: map_at_5
916
+ value: 0.3111
917
+ - type: mrr_at_1
918
+ value: 0.26248
919
+ - type: mrr_at_10
920
+ value: 0.34149
921
+ - type: mrr_at_100
922
+ value: 0.35189
923
+ - type: mrr_at_1000
924
+ value: 0.35251
925
+ - type: mrr_at_3
926
+ value: 0.31639
927
+ - type: mrr_at_5
928
+ value: 0.33182
929
+ - type: ndcg_at_1
930
+ value: 0.26248
931
+ - type: ndcg_at_10
932
+ value: 0.36889
933
+ - type: ndcg_at_100
934
+ value: 0.42426
935
+ - type: ndcg_at_1000
936
+ value: 0.44745
937
+ - type: ndcg_at_3
938
+ value: 0.31799
939
+ - type: ndcg_at_5
940
+ value: 0.34563
941
+ - type: precision_at_1
942
+ value: 0.26248
943
+ - type: precision_at_10
944
+ value: 0.05712
945
+ - type: precision_at_100
946
+ value: 0.00915
947
+ - type: precision_at_1000
948
+ value: 0.00123
949
+ - type: precision_at_3
950
+ value: 0.13309
951
+ - type: precision_at_5
952
+ value: 0.09649
953
+ - type: recall_at_1
954
+ value: 0.24067
955
+ - type: recall_at_10
956
+ value: 0.49344
957
+ - type: recall_at_100
958
+ value: 0.7412
959
+ - type: recall_at_1000
960
+ value: 0.91276
961
+ - type: recall_at_3
962
+ value: 0.36272
963
+ - type: recall_at_5
964
+ value: 0.4277
965
+ - dataset:
966
+ type: mteb/dbpedia
967
+ name: MTEB DBPedia
968
+ config: default
969
+ split: test
970
+ task:
971
+ type: Retrieval
972
+ metrics:
973
+ - type: map_at_1
974
+ value: 0.08651
975
+ - type: map_at_10
976
+ value: 0.17628
977
+ - type: map_at_100
978
+ value: 0.23354
979
+ - type: map_at_1000
980
+ value: 0.24827
981
+ - type: map_at_3
982
+ value: 0.1351
983
+ - type: map_at_5
984
+ value: 0.15468
985
+ - type: mrr_at_1
986
+ value: 0.645
987
+ - type: mrr_at_10
988
+ value: 0.71989
989
+ - type: mrr_at_100
990
+ value: 0.72332
991
+ - type: mrr_at_1000
992
+ value: 0.72346
993
+ - type: mrr_at_3
994
+ value: 0.7025
995
+ - type: mrr_at_5
996
+ value: 0.71275
997
+ - type: ndcg_at_1
998
+ value: 0.51375
999
+ - type: ndcg_at_10
1000
+ value: 0.3596
1001
+ - type: ndcg_at_100
1002
+ value: 0.39878
1003
+ - type: ndcg_at_1000
1004
+ value: 0.47931
1005
+ - type: ndcg_at_3
1006
+ value: 0.41275
1007
+ - type: ndcg_at_5
1008
+ value: 0.38297
1009
+ - type: precision_at_1
1010
+ value: 0.645
1011
+ - type: precision_at_10
1012
+ value: 0.2745
1013
+ - type: precision_at_100
1014
+ value: 0.08405
1015
+ - type: precision_at_1000
1016
+ value: 0.01923
1017
+ - type: precision_at_3
1018
+ value: 0.44417
1019
+ - type: precision_at_5
1020
+ value: 0.366
1021
+ - type: recall_at_1
1022
+ value: 0.08651
1023
+ - type: recall_at_10
1024
+ value: 0.22416
1025
+ - type: recall_at_100
1026
+ value: 0.46381
1027
+ - type: recall_at_1000
1028
+ value: 0.71557
1029
+ - type: recall_at_3
1030
+ value: 0.14847
1031
+ - type: recall_at_5
1032
+ value: 0.1804
1033
+ - dataset:
1034
+ type: mteb/fever
1035
+ name: MTEB FEVER
1036
+ config: default
1037
+ split: test
1038
+ task:
1039
+ type: Retrieval
1040
+ metrics:
1041
+ - type: map_at_1
1042
+ value: 0.73211
1043
+ - type: map_at_10
1044
+ value: 0.81463
1045
+ - type: map_at_100
1046
+ value: 0.81622
1047
+ - type: map_at_1000
1048
+ value: 0.81634
1049
+ - type: map_at_3
1050
+ value: 0.805
1051
+ - type: map_at_5
1052
+ value: 0.81134
1053
+ - type: mrr_at_1
1054
+ value: 0.79088
1055
+ - type: mrr_at_10
1056
+ value: 0.86943
1057
+ - type: mrr_at_100
1058
+ value: 0.87017
1059
+ - type: mrr_at_1000
1060
+ value: 0.87018
1061
+ - type: mrr_at_3
1062
+ value: 0.86154
1063
+ - type: mrr_at_5
1064
+ value: 0.867
1065
+ - type: ndcg_at_1
1066
+ value: 0.79088
1067
+ - type: ndcg_at_10
1068
+ value: 0.85528
1069
+ - type: ndcg_at_100
1070
+ value: 0.86134
1071
+ - type: ndcg_at_1000
1072
+ value: 0.86367
1073
+ - type: ndcg_at_3
1074
+ value: 0.83943
1075
+ - type: ndcg_at_5
1076
+ value: 0.84878
1077
+ - type: precision_at_1
1078
+ value: 0.79088
1079
+ - type: precision_at_10
1080
+ value: 0.10132
1081
+ - type: precision_at_100
1082
+ value: 0.01055
1083
+ - type: precision_at_1000
1084
+ value: 0.00109
1085
+ - type: precision_at_3
1086
+ value: 0.31963
1087
+ - type: precision_at_5
1088
+ value: 0.19769
1089
+ - type: recall_at_1
1090
+ value: 0.73211
1091
+ - type: recall_at_10
1092
+ value: 0.92797
1093
+ - type: recall_at_100
1094
+ value: 0.95263
1095
+ - type: recall_at_1000
1096
+ value: 0.96738
1097
+ - type: recall_at_3
1098
+ value: 0.88328
1099
+ - type: recall_at_5
1100
+ value: 0.90821
1101
+ - dataset:
1102
+ type: mteb/fiqa
1103
+ name: MTEB FiQA2018
1104
+ config: default
1105
+ split: test
1106
+ task:
1107
+ type: Retrieval
1108
+ metrics:
1109
+ - type: map_at_1
1110
+ value: 0.18311
1111
+ - type: map_at_10
1112
+ value: 0.29201
1113
+ - type: map_at_100
1114
+ value: 0.3093
1115
+ - type: map_at_1000
1116
+ value: 0.31116
1117
+ - type: map_at_3
1118
+ value: 0.24778
1119
+ - type: map_at_5
1120
+ value: 0.27453
1121
+ - type: mrr_at_1
1122
+ value: 0.35494
1123
+ - type: mrr_at_10
1124
+ value: 0.44489
1125
+ - type: mrr_at_100
1126
+ value: 0.4532
1127
+ - type: mrr_at_1000
1128
+ value: 0.45369
1129
+ - type: mrr_at_3
1130
+ value: 0.41667
1131
+ - type: mrr_at_5
1132
+ value: 0.43418
1133
+ - type: ndcg_at_1
1134
+ value: 0.35494
1135
+ - type: ndcg_at_10
1136
+ value: 0.36868
1137
+ - type: ndcg_at_100
1138
+ value: 0.43463
1139
+ - type: ndcg_at_1000
1140
+ value: 0.46766
1141
+ - type: ndcg_at_3
1142
+ value: 0.32305
1143
+ - type: ndcg_at_5
1144
+ value: 0.34332
1145
+ - type: precision_at_1
1146
+ value: 0.35494
1147
+ - type: precision_at_10
1148
+ value: 0.10324
1149
+ - type: precision_at_100
1150
+ value: 0.01707
1151
+ - type: precision_at_1000
1152
+ value: 0.00229
1153
+ - type: precision_at_3
1154
+ value: 0.21142
1155
+ - type: precision_at_5
1156
+ value: 0.16327
1157
+ - type: recall_at_1
1158
+ value: 0.18311
1159
+ - type: recall_at_10
1160
+ value: 0.43881
1161
+ - type: recall_at_100
1162
+ value: 0.68593
1163
+ - type: recall_at_1000
1164
+ value: 0.8855
1165
+ - type: recall_at_3
1166
+ value: 0.28824
1167
+ - type: recall_at_5
1168
+ value: 0.36178
1169
+ - dataset:
1170
+ type: mteb/hotpotqa
1171
+ name: MTEB HotpotQA
1172
+ config: default
1173
+ split: test
1174
+ task:
1175
+ type: Retrieval
1176
+ metrics:
1177
+ - type: map_at_1
1178
+ value: 0.36766
1179
+ - type: map_at_10
1180
+ value: 0.53639
1181
+ - type: map_at_100
1182
+ value: 0.54532
1183
+ - type: map_at_1000
1184
+ value: 0.54608
1185
+ - type: map_at_3
1186
+ value: 0.50427
1187
+ - type: map_at_5
1188
+ value: 0.5245
1189
+ - type: mrr_at_1
1190
+ value: 0.73531
1191
+ - type: mrr_at_10
1192
+ value: 0.80104
1193
+ - type: mrr_at_100
1194
+ value: 0.80341
1195
+ - type: mrr_at_1000
1196
+ value: 0.80351
1197
+ - type: mrr_at_3
1198
+ value: 0.78949
1199
+ - type: mrr_at_5
1200
+ value: 0.79729
1201
+ - type: ndcg_at_1
1202
+ value: 0.73531
1203
+ - type: ndcg_at_10
1204
+ value: 0.62918
1205
+ - type: ndcg_at_100
1206
+ value: 0.66056
1207
+ - type: ndcg_at_1000
1208
+ value: 0.67554
1209
+ - type: ndcg_at_3
1210
+ value: 0.58247
1211
+ - type: ndcg_at_5
1212
+ value: 0.60905
1213
+ - type: precision_at_1
1214
+ value: 0.73531
1215
+ - type: precision_at_10
1216
+ value: 0.1302
1217
+ - type: precision_at_100
1218
+ value: 0.01546
1219
+ - type: precision_at_1000
1220
+ value: 0.00175
1221
+ - type: precision_at_3
1222
+ value: 0.36556
1223
+ - type: precision_at_5
1224
+ value: 0.24032
1225
+ - type: recall_at_1
1226
+ value: 0.36766
1227
+ - type: recall_at_10
1228
+ value: 0.65098
1229
+ - type: recall_at_100
1230
+ value: 0.77306
1231
+ - type: recall_at_1000
1232
+ value: 0.87252
1233
+ - type: recall_at_3
1234
+ value: 0.54835
1235
+ - type: recall_at_5
1236
+ value: 0.60081
1237
+ - dataset:
1238
+ type: mteb/msmarco
1239
+ name: MTEB MSMARCO
1240
+ config: default
1241
+ split: dev
1242
+ task:
1243
+ type: Retrieval
1244
+ metrics:
1245
+ - type: map_at_1
1246
+ value: 0.14654
1247
+ - type: map_at_10
1248
+ value: 0.2472
1249
+ - type: map_at_100
1250
+ value: 0.25994
1251
+ - type: map_at_1000
1252
+ value: 0.26067
1253
+ - type: map_at_3
1254
+ value: 0.21234
1255
+ - type: map_at_5
1256
+ value: 0.2319
1257
+ - type: mrr_at_1
1258
+ value: 0.15086
1259
+ - type: mrr_at_10
1260
+ value: 0.25184
1261
+ - type: mrr_at_100
1262
+ value: 0.26422
1263
+ - type: mrr_at_1000
1264
+ value: 0.26489
1265
+ - type: mrr_at_3
1266
+ value: 0.21731
1267
+ - type: mrr_at_5
1268
+ value: 0.23674
1269
+ - type: ndcg_at_1
1270
+ value: 0.15086
1271
+ - type: ndcg_at_10
1272
+ value: 0.30711
1273
+ - type: ndcg_at_100
1274
+ value: 0.37221
1275
+ - type: ndcg_at_1000
1276
+ value: 0.39133
1277
+ - type: ndcg_at_3
1278
+ value: 0.23567
1279
+ - type: ndcg_at_5
1280
+ value: 0.27066
1281
+ - type: precision_at_1
1282
+ value: 0.15086
1283
+ - type: precision_at_10
1284
+ value: 0.05132
1285
+ - type: precision_at_100
1286
+ value: 0.00845
1287
+ - type: precision_at_1000
1288
+ value: 0.00101
1289
+ - type: precision_at_3
1290
+ value: 0.10277
1291
+ - type: precision_at_5
1292
+ value: 0.07923
1293
+ - type: recall_at_1
1294
+ value: 0.14654
1295
+ - type: recall_at_10
1296
+ value: 0.49341
1297
+ - type: recall_at_100
1298
+ value: 0.80224
1299
+ - type: recall_at_1000
1300
+ value: 0.95037
1301
+ - type: recall_at_3
1302
+ value: 0.29862
1303
+ - type: recall_at_5
1304
+ value: 0.38274
1305
+ - dataset:
1306
+ type: mteb/nfcorpus
1307
+ name: MTEB NFCorpus
1308
+ config: default
1309
+ split: test
1310
+ task:
1311
+ type: Retrieval
1312
+ metrics:
1313
+ - type: map_at_1
1314
+ value: 0.05452
1315
+ - type: map_at_10
1316
+ value: 0.12758
1317
+ - type: map_at_100
1318
+ value: 0.1593
1319
+ - type: map_at_1000
1320
+ value: 0.17422
1321
+ - type: map_at_3
1322
+ value: 0.0945
1323
+ - type: map_at_5
1324
+ value: 0.1092
1325
+ - type: mrr_at_1
1326
+ value: 0.43963
1327
+ - type: mrr_at_10
1328
+ value: 0.53237
1329
+ - type: mrr_at_100
1330
+ value: 0.53777
1331
+ - type: mrr_at_1000
1332
+ value: 0.53822
1333
+ - type: mrr_at_3
1334
+ value: 0.51445
1335
+ - type: mrr_at_5
1336
+ value: 0.52466
1337
+ - type: ndcg_at_1
1338
+ value: 0.41486
1339
+ - type: ndcg_at_10
1340
+ value: 0.33737
1341
+ - type: ndcg_at_100
1342
+ value: 0.30886
1343
+ - type: ndcg_at_1000
1344
+ value: 0.40018
1345
+ - type: ndcg_at_3
1346
+ value: 0.39324
1347
+ - type: ndcg_at_5
1348
+ value: 0.36949
1349
+ - type: precision_at_1
1350
+ value: 0.43344
1351
+ - type: precision_at_10
1352
+ value: 0.24799
1353
+ - type: precision_at_100
1354
+ value: 0.07895
1355
+ - type: precision_at_1000
1356
+ value: 0.02091
1357
+ - type: precision_at_3
1358
+ value: 0.37152
1359
+ - type: precision_at_5
1360
+ value: 0.31703
1361
+ - type: recall_at_1
1362
+ value: 0.05452
1363
+ - type: recall_at_10
1364
+ value: 0.1712
1365
+ - type: recall_at_100
1366
+ value: 0.30719
1367
+ - type: recall_at_1000
1368
+ value: 0.62766
1369
+ - type: recall_at_3
1370
+ value: 0.10733
1371
+ - type: recall_at_5
1372
+ value: 0.13553
1373
+ - dataset:
1374
+ type: mteb/nq
1375
+ name: MTEB NQ
1376
+ config: default
1377
+ split: test
1378
+ task:
1379
+ type: Retrieval
1380
+ metrics:
1381
+ - type: map_at_1
1382
+ value: 0.29022
1383
+ - type: map_at_10
1384
+ value: 0.4373
1385
+ - type: map_at_100
1386
+ value: 0.44849
1387
+ - type: map_at_1000
1388
+ value: 0.44877
1389
+ - type: map_at_3
1390
+ value: 0.39045
1391
+ - type: map_at_5
1392
+ value: 0.4186
1393
+ - type: mrr_at_1
1394
+ value: 0.32793
1395
+ - type: mrr_at_10
1396
+ value: 0.46243
1397
+ - type: mrr_at_100
1398
+ value: 0.47083
1399
+ - type: mrr_at_1000
1400
+ value: 0.47101
1401
+ - type: mrr_at_3
1402
+ value: 0.42261
1403
+ - type: mrr_at_5
1404
+ value: 0.44775
1405
+ - type: ndcg_at_1
1406
+ value: 0.32793
1407
+ - type: ndcg_at_10
1408
+ value: 0.51631
1409
+ - type: ndcg_at_100
1410
+ value: 0.56287
1411
+ - type: ndcg_at_1000
1412
+ value: 0.56949
1413
+ - type: ndcg_at_3
1414
+ value: 0.42782
1415
+ - type: ndcg_at_5
1416
+ value: 0.47554
1417
+ - type: precision_at_1
1418
+ value: 0.32793
1419
+ - type: precision_at_10
1420
+ value: 0.08737
1421
+ - type: precision_at_100
1422
+ value: 0.01134
1423
+ - type: precision_at_1000
1424
+ value: 0.0012
1425
+ - type: precision_at_3
1426
+ value: 0.19583
1427
+ - type: precision_at_5
1428
+ value: 0.14484
1429
+ - type: recall_at_1
1430
+ value: 0.29022
1431
+ - type: recall_at_10
1432
+ value: 0.73325
1433
+ - type: recall_at_100
1434
+ value: 0.93455
1435
+ - type: recall_at_1000
1436
+ value: 0.98414
1437
+ - type: recall_at_3
1438
+ value: 0.50406
1439
+ - type: recall_at_5
1440
+ value: 0.6145
1441
+ - dataset:
1442
+ type: mteb/quora
1443
+ name: MTEB QuoraRetrieval
1444
+ config: default
1445
+ split: test
1446
+ task:
1447
+ type: Retrieval
1448
+ metrics:
1449
+ - type: map_at_1
1450
+ value: 0.68941
1451
+ - type: map_at_10
1452
+ value: 0.82641
1453
+ - type: map_at_100
1454
+ value: 0.83317
1455
+ - type: map_at_1000
1456
+ value: 0.83337
1457
+ - type: map_at_3
1458
+ value: 0.79604
1459
+ - type: map_at_5
1460
+ value: 0.81525
1461
+ - type: mrr_at_1
1462
+ value: 0.7935
1463
+ - type: mrr_at_10
1464
+ value: 0.85969
1465
+ - type: mrr_at_100
1466
+ value: 0.86094
1467
+ - type: mrr_at_1000
1468
+ value: 0.86095
1469
+ - type: mrr_at_3
1470
+ value: 0.84852
1471
+ - type: mrr_at_5
1472
+ value: 0.85627
1473
+ - type: ndcg_at_1
1474
+ value: 0.7936
1475
+ - type: ndcg_at_10
1476
+ value: 0.86687
1477
+ - type: ndcg_at_100
1478
+ value: 0.88094
1479
+ - type: ndcg_at_1000
1480
+ value: 0.88243
1481
+ - type: ndcg_at_3
1482
+ value: 0.83538
1483
+ - type: ndcg_at_5
1484
+ value: 0.85308
1485
+ - type: precision_at_1
1486
+ value: 0.7936
1487
+ - type: precision_at_10
1488
+ value: 0.13145
1489
+ - type: precision_at_100
1490
+ value: 0.01517
1491
+ - type: precision_at_1000
1492
+ value: 0.00156
1493
+ - type: precision_at_3
1494
+ value: 0.36353
1495
+ - type: precision_at_5
1496
+ value: 0.24044
1497
+ - type: recall_at_1
1498
+ value: 0.68941
1499
+ - type: recall_at_10
1500
+ value: 0.94407
1501
+ - type: recall_at_100
1502
+ value: 0.99226
1503
+ - type: recall_at_1000
1504
+ value: 0.99958
1505
+ - type: recall_at_3
1506
+ value: 0.85502
1507
+ - type: recall_at_5
1508
+ value: 0.90372
1509
+ - dataset:
1510
+ type: mteb/scidocs
1511
+ name: MTEB SCIDOCS
1512
+ config: default
1513
+ split: test
1514
+ task:
1515
+ type: Retrieval
1516
+ metrics:
1517
+ - type: map_at_1
1518
+ value: 0.04988
1519
+ - type: map_at_10
1520
+ value: 0.13553
1521
+ - type: map_at_100
1522
+ value: 0.16136
1523
+ - type: map_at_1000
1524
+ value: 0.16512
1525
+ - type: map_at_3
1526
+ value: 0.09439
1527
+ - type: map_at_5
1528
+ value: 0.1146
1529
+ - type: mrr_at_1
1530
+ value: 0.246
1531
+ - type: mrr_at_10
1532
+ value: 0.36792
1533
+ - type: mrr_at_100
1534
+ value: 0.37973
1535
+ - type: mrr_at_1000
1536
+ value: 0.38011
1537
+ - type: mrr_at_3
1538
+ value: 0.33117
1539
+ - type: mrr_at_5
1540
+ value: 0.35172
1541
+ - type: ndcg_at_1
1542
+ value: 0.246
1543
+ - type: ndcg_at_10
1544
+ value: 0.22542
1545
+ - type: ndcg_at_100
1546
+ value: 0.32326
1547
+ - type: ndcg_at_1000
1548
+ value: 0.3828
1549
+ - type: ndcg_at_3
1550
+ value: 0.20896
1551
+ - type: ndcg_at_5
1552
+ value: 0.18497
1553
+ - type: precision_at_1
1554
+ value: 0.246
1555
+ - type: precision_at_10
1556
+ value: 0.1194
1557
+ - type: precision_at_100
1558
+ value: 0.02616
1559
+ - type: precision_at_1000
1560
+ value: 0.00404
1561
+ - type: precision_at_3
1562
+ value: 0.198
1563
+ - type: precision_at_5
1564
+ value: 0.1654
1565
+ - type: recall_at_1
1566
+ value: 0.04988
1567
+ - type: recall_at_10
1568
+ value: 0.24212
1569
+ - type: recall_at_100
1570
+ value: 0.53105
1571
+ - type: recall_at_1000
1572
+ value: 0.82022
1573
+ - type: recall_at_3
1574
+ value: 0.12047
1575
+ - type: recall_at_5
1576
+ value: 0.16777
1577
+ - dataset:
1578
+ type: mteb/scifact
1579
+ name: MTEB SciFact
1580
+ config: default
1581
+ split: test
1582
+ task:
1583
+ type: Retrieval
1584
+ metrics:
1585
+ - type: map_at_1
1586
+ value: 0.56578
1587
+ - type: map_at_10
1588
+ value: 0.66725
1589
+ - type: map_at_100
1590
+ value: 0.67379
1591
+ - type: map_at_1000
1592
+ value: 0.674
1593
+ - type: map_at_3
1594
+ value: 0.63416
1595
+ - type: map_at_5
1596
+ value: 0.6577
1597
+ - type: mrr_at_1
1598
+ value: 0.59333
1599
+ - type: mrr_at_10
1600
+ value: 0.67533
1601
+ - type: mrr_at_100
1602
+ value: 0.68062
1603
+ - type: mrr_at_1000
1604
+ value: 0.68082
1605
+ - type: mrr_at_3
1606
+ value: 0.64944
1607
+ - type: mrr_at_5
1608
+ value: 0.66928
1609
+ - type: ndcg_at_1
1610
+ value: 0.59333
1611
+ - type: ndcg_at_10
1612
+ value: 0.7127
1613
+ - type: ndcg_at_100
1614
+ value: 0.73889
1615
+ - type: ndcg_at_1000
1616
+ value: 0.7441
1617
+ - type: ndcg_at_3
1618
+ value: 0.65793
1619
+ - type: ndcg_at_5
1620
+ value: 0.69429
1621
+ - type: precision_at_1
1622
+ value: 0.59333
1623
+ - type: precision_at_10
1624
+ value: 0.096
1625
+ - type: precision_at_100
1626
+ value: 0.01087
1627
+ - type: precision_at_1000
1628
+ value: 0.00113
1629
+ - type: precision_at_3
1630
+ value: 0.25556
1631
+ - type: precision_at_5
1632
+ value: 0.17667
1633
+ - type: recall_at_1
1634
+ value: 0.56578
1635
+ - type: recall_at_10
1636
+ value: 0.842
1637
+ - type: recall_at_100
1638
+ value: 0.95667
1639
+ - type: recall_at_1000
1640
+ value: 0.99667
1641
+ - type: recall_at_3
1642
+ value: 0.70072
1643
+ - type: recall_at_5
1644
+ value: 0.79011
1645
+ - dataset:
1646
+ type: mteb/touche2020
1647
+ name: MTEB Touche2020
1648
+ config: default
1649
+ split: test
1650
+ task:
1651
+ type: Retrieval
1652
+ metrics:
1653
+ - type: map_at_1
1654
+ value: 0.01976
1655
+ - type: map_at_10
1656
+ value: 0.09688
1657
+ - type: map_at_100
1658
+ value: 0.15117
1659
+ - type: map_at_1000
1660
+ value: 0.16769
1661
+ - type: map_at_3
1662
+ value: 0.04589
1663
+ - type: map_at_5
1664
+ value: 0.06556
1665
+ - type: mrr_at_1
1666
+ value: 0.26531
1667
+ - type: mrr_at_10
1668
+ value: 0.43863
1669
+ - type: mrr_at_100
1670
+ value: 0.44767
1671
+ - type: mrr_at_1000
1672
+ value: 0.44767
1673
+ - type: mrr_at_3
1674
+ value: 0.39116
1675
+ - type: mrr_at_5
1676
+ value: 0.41156
1677
+ - type: ndcg_at_1
1678
+ value: 0.23469
1679
+ - type: ndcg_at_10
1680
+ value: 0.24029
1681
+ - type: ndcg_at_100
1682
+ value: 0.34425
1683
+ - type: ndcg_at_1000
1684
+ value: 0.46907
1685
+ - type: ndcg_at_3
1686
+ value: 0.25522
1687
+ - type: ndcg_at_5
1688
+ value: 0.24333
1689
+ - type: precision_at_1
1690
+ value: 0.26531
1691
+ - type: precision_at_10
1692
+ value: 0.22449
1693
+ - type: precision_at_100
1694
+ value: 0.07122
1695
+ - type: precision_at_1000
1696
+ value: 0.01527
1697
+ - type: precision_at_3
1698
+ value: 0.27891
1699
+ - type: precision_at_5
1700
+ value: 0.25714
1701
+ - type: recall_at_1
1702
+ value: 0.01976
1703
+ - type: recall_at_10
1704
+ value: 0.16633
1705
+ - type: recall_at_100
1706
+ value: 0.4561
1707
+ - type: recall_at_1000
1708
+ value: 0.82481
1709
+ - type: recall_at_3
1710
+ value: 0.06101
1711
+ - type: recall_at_5
1712
+ value: 0.0968
1713
+ - dataset:
1714
+ type: mteb/trec-covid
1715
+ name: MTEB TRECCOVID
1716
+ config: default
1717
+ split: test
1718
+ task:
1719
+ type: Retrieval
1720
+ metrics:
1721
+ - type: map_at_1
1722
+ value: 0.00211
1723
+ - type: map_at_10
1724
+ value: 0.01526
1725
+ - type: map_at_100
1726
+ value: 0.08863
1727
+ - type: map_at_1000
1728
+ value: 0.23162
1729
+ - type: map_at_3
1730
+ value: 0.00555
1731
+ - type: map_at_5
1732
+ value: 0.00873
1733
+ - type: mrr_at_1
1734
+ value: 0.76
1735
+ - type: mrr_at_10
1736
+ value: 0.8485
1737
+ - type: mrr_at_100
1738
+ value: 0.8485
1739
+ - type: mrr_at_1000
1740
+ value: 0.8485
1741
+ - type: mrr_at_3
1742
+ value: 0.84
1743
+ - type: mrr_at_5
1744
+ value: 0.844
1745
+ - type: ndcg_at_1
1746
+ value: 0.7
1747
+ - type: ndcg_at_10
1748
+ value: 0.63098
1749
+ - type: ndcg_at_100
1750
+ value: 0.49847
1751
+ - type: ndcg_at_1000
1752
+ value: 0.48395
1753
+ - type: ndcg_at_3
1754
+ value: 0.68704
1755
+ - type: ndcg_at_5
1756
+ value: 0.67533
1757
+ - type: precision_at_1
1758
+ value: 0.76
1759
+ - type: precision_at_10
1760
+ value: 0.66
1761
+ - type: precision_at_100
1762
+ value: 0.5134
1763
+ - type: precision_at_1000
1764
+ value: 0.2168
1765
+ - type: precision_at_3
1766
+ value: 0.72667
1767
+ - type: precision_at_5
1768
+ value: 0.716
1769
+ - type: recall_at_1
1770
+ value: 0.00211
1771
+ - type: recall_at_10
1772
+ value: 0.01748
1773
+ - type: recall_at_100
1774
+ value: 0.12448
1775
+ - type: recall_at_1000
1776
+ value: 0.46795
1777
+ - type: recall_at_3
1778
+ value: 0.00593
1779
+ - type: recall_at_5
1780
+ value: 0.00962
1781
+ ---
 
1782
  # Granite-Embedding-30m-English
1783
 
1784
  **Model Summary:**
1785
+ Granite-Embedding-30m-English is a 30M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning, knowledge distillation and model merging for improved performance.
1786
 
1787
  - **Developers:** Granite Embedding Team, IBM
1788
+ - **GitHub Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models)
1789
  - **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
1790
+ - **Paper:** Coming Soon
1791
  - **Release Date**: December 18th, 2024
1792
  - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
1793
 
 
1874
  ```
1875
  **Evaluation:**
1876
 
1877
+ Granite-Embedding-30M-English is twice as fast as other models with similar embedding dimensions, while maintaining competitive performance. The performance of the Granite-Embedding-30M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below.
1878
 
1879
+ | Model | Paramters (M)| Embedding Dimension | MTEB Retrieval (15) | CoIR (10) |
1880
+ |---------------------------------|:------------:|:-------------------:|:-------------------: |:----------:|
1881
+ |granite-embedding-30m-english |30 |384 |49.1 |47.0 |
1882
 
1883
 
1884
  **Model Architecture:**