tomaarsen HF staff commited on
Commit
cc989c9
1 Parent(s): 51f2739

Improve model metadata

Browse files

Hello!

## Pull Request overview
* Improve model metadata

## Details
This should make it a bit easier to find the model.

- Tom Aarsen

Files changed (1) hide show
  1. README.md +122 -119
README.md CHANGED
@@ -1,6 +1,8 @@
1
  ---
2
  tags:
3
  - mteb
 
 
4
  model-index:
5
  - name: NV-Embed-v2
6
  results:
@@ -89,17 +91,17 @@ model-index:
89
  - type: map_at_5
90
  value: 61.027
91
  - type: mrr_at_1
92
- value: 0.0
93
  - type: mrr_at_10
94
- value: 0.0
95
  - type: mrr_at_100
96
- value: 0.0
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  - type: mrr_at_1000
98
- value: 0.0
99
  - type: mrr_at_3
100
- value: 0.0
101
  - type: mrr_at_5
102
- value: 0.0
103
  - type: ndcg_at_1
104
  value: 46.515
105
  - type: ndcg_at_10
@@ -277,17 +279,17 @@ model-index:
277
  - type: map_at_5
278
  value: 42.317083333333336
279
  - type: mrr_at_1
280
- value: 0.0
281
  - type: mrr_at_10
282
- value: 0.0
283
  - type: mrr_at_100
284
- value: 0.0
285
  - type: mrr_at_1000
286
- value: 0.0
287
  - type: mrr_at_3
288
- value: 0.0
289
  - type: mrr_at_5
290
- value: 0.0
291
  - type: ndcg_at_1
292
  value: 38.30616666666667
293
  - type: ndcg_at_10
@@ -348,17 +350,17 @@ model-index:
348
  - type: map_at_5
349
  value: 31.955
350
  - type: mrr_at_1
351
- value: 0.0
352
  - type: mrr_at_10
353
- value: 0.0
354
  - type: mrr_at_100
355
- value: 0.0
356
  - type: mrr_at_1000
357
- value: 0.0
358
  - type: mrr_at_3
359
- value: 0.0
360
  - type: mrr_at_5
361
- value: 0.0
362
  - type: ndcg_at_1
363
  value: 44.104
364
  - type: ndcg_at_10
@@ -419,19 +421,19 @@ model-index:
419
  - type: map_at_5
420
  value: 21.062
421
  - type: mrr_at_1
422
- value: 0.0
423
  - type: mrr_at_10
424
- value: 0.0
425
  - type: mrr_at_100
426
- value: 0.0
427
  - type: mrr_at_1000
428
- value: 0.0
429
  - type: mrr_at_3
430
- value: 0.0
431
  - type: mrr_at_5
432
- value: 0.0
433
  - type: ndcg_at_1
434
- value: 66.0
435
  - type: ndcg_at_10
436
  value: 53.496
437
  - type: ndcg_at_100
@@ -451,7 +453,7 @@ model-index:
451
  - type: precision_at_1000
452
  value: 2.5940000000000003
453
  - type: precision_at_3
454
- value: 61.0
455
  - type: precision_at_5
456
  value: 54.65
457
  - type: recall_at_1
@@ -509,17 +511,17 @@ model-index:
509
  - type: map_at_5
510
  value: 91.262
511
  - type: mrr_at_1
512
- value: 0.0
513
  - type: mrr_at_10
514
- value: 0.0
515
  - type: mrr_at_100
516
- value: 0.0
517
  - type: mrr_at_1000
518
- value: 0.0
519
  - type: mrr_at_3
520
- value: 0.0
521
  - type: mrr_at_5
522
- value: 0.0
523
  - type: ndcg_at_1
524
  value: 91.20899999999999
525
  - type: ndcg_at_10
@@ -580,17 +582,17 @@ model-index:
580
  - type: map_at_5
581
  value: 55.054
582
  - type: mrr_at_1
583
- value: 0.0
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  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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  - type: mrr_at_1000
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- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
593
- value: 0.0
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  - type: ndcg_at_1
595
  value: 64.815
596
  - type: ndcg_at_10
@@ -651,17 +653,17 @@ model-index:
651
  - type: map_at_5
652
  value: 78.935
653
  - type: mrr_at_1
654
- value: 0.0
655
  - type: mrr_at_10
656
- value: 0.0
657
  - type: mrr_at_100
658
- value: 0.0
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  - type: mrr_at_1000
660
- value: 0.0
661
  - type: mrr_at_3
662
- value: 0.0
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  - type: mrr_at_5
664
- value: 0.0
665
  - type: ndcg_at_1
666
  value: 89.305
667
  - type: ndcg_at_10
@@ -733,65 +735,65 @@ model-index:
733
  type: mteb/msmarco
734
  metrics:
735
  - type: map_at_1
736
- value: 0.0
737
  - type: map_at_10
738
  value: 38.342
739
  - type: map_at_100
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- value: 0.0
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  - type: map_at_1000
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- value: 0.0
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  - type: map_at_3
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- value: 0.0
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  - type: map_at_5
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- value: 0.0
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  - type: mrr_at_1
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- value: 0.0
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  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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  - type: mrr_at_1000
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- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
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- value: 0.0
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  - type: ndcg_at_1
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- value: 0.0
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  - type: ndcg_at_10
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  value: 45.629999999999995
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  - type: ndcg_at_100
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- value: 0.0
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  - type: ndcg_at_1000
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- value: 0.0
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  - type: ndcg_at_3
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- value: 0.0
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  - type: ndcg_at_5
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- value: 0.0
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  - type: precision_at_1
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- value: 0.0
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  - type: precision_at_10
774
  value: 7.119000000000001
775
  - type: precision_at_100
776
- value: 0.0
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  - type: precision_at_1000
778
- value: 0.0
779
  - type: precision_at_3
780
- value: 0.0
781
  - type: precision_at_5
782
- value: 0.0
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  - type: recall_at_1
784
- value: 0.0
785
  - type: recall_at_10
786
  value: 67.972
787
  - type: recall_at_100
788
- value: 0.0
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  - type: recall_at_1000
790
- value: 0.0
791
  - type: recall_at_3
792
- value: 0.0
793
  - type: recall_at_5
794
- value: 0.0
795
  - type: main_score
796
  value: 45.629999999999995
797
  task:
@@ -937,17 +939,17 @@ model-index:
937
  - type: map_at_5
938
  value: 15.171000000000001
939
  - type: mrr_at_1
940
- value: 0.0
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  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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  - type: mrr_at_1000
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- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
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- value: 0.0
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  - type: ndcg_at_1
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  value: 55.728
953
  - type: ndcg_at_10
@@ -1008,17 +1010,17 @@ model-index:
1008
  - type: map_at_5
1009
  value: 65.364
1010
  - type: mrr_at_1
1011
- value: 0.0
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  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
1021
- value: 0.0
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  - type: ndcg_at_1
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  value: 55.417
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  - type: ndcg_at_10
@@ -1079,17 +1081,17 @@ model-index:
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  - type: map_at_5
1080
  value: 84.396
1081
  - type: mrr_at_1
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- value: 0.0
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  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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- value: 0.0
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- value: 0.0
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- value: 0.0
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  - type: ndcg_at_1
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  value: 82.12
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  - type: ndcg_at_10
@@ -1180,17 +1182,17 @@ model-index:
1180
  - type: map_at_5
1181
  value: 11.158
1182
  - type: mrr_at_1
1183
- value: 0.0
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  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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  - type: mrr_at_1000
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- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
1193
- value: 0.0
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  - type: ndcg_at_1
1195
  value: 26.3
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  - type: ndcg_at_10
@@ -1473,19 +1475,19 @@ model-index:
1473
  - type: map_at_5
1474
  value: 74.74
1475
  - type: mrr_at_1
1476
- value: 0.0
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  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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  - type: mrr_at_1000
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- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
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- value: 0.0
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  - type: ndcg_at_1
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- value: 66.0
1489
  - type: ndcg_at_10
1490
  value: 80.12700000000001
1491
  - type: ndcg_at_100
@@ -1497,7 +1499,7 @@ model-index:
1497
  - type: ndcg_at_5
1498
  value: 78.827
1499
  - type: precision_at_1
1500
- value: 66.0
1501
  - type: precision_at_10
1502
  value: 10.567
1503
  - type: precision_at_100
@@ -1515,7 +1517,7 @@ model-index:
1515
  - type: recall_at_100
1516
  value: 98.667
1517
  - type: recall_at_1000
1518
- value: 100.0
1519
  - type: recall_at_3
1520
  value: 83.322
1521
  - type: recall_at_5
@@ -1679,19 +1681,19 @@ model-index:
1679
  - type: map_at_5
1680
  value: 1.185
1681
  - type: mrr_at_1
1682
- value: 0.0
1683
  - type: mrr_at_10
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- value: 0.0
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  - type: mrr_at_100
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- value: 0.0
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  - type: mrr_at_1000
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- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
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- value: 0.0
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  - type: ndcg_at_1
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- value: 91.0
1695
  - type: ndcg_at_10
1696
  value: 88.442
1697
  - type: ndcg_at_100
@@ -1703,7 +1705,7 @@ model-index:
1703
  - type: ndcg_at_5
1704
  value: 89.562
1705
  - type: precision_at_1
1706
- value: 92.0
1707
  - type: precision_at_10
1708
  value: 92.60000000000001
1709
  - type: precision_at_100
@@ -1711,7 +1713,7 @@ model-index:
1711
  - type: precision_at_1000
1712
  value: 28.222
1713
  - type: precision_at_3
1714
- value: 94.0
1715
  - type: precision_at_5
1716
  value: 93.60000000000001
1717
  - type: recall_at_1
@@ -1750,17 +1752,17 @@ model-index:
1750
  - type: map_at_5
1751
  value: 9.49
1752
  - type: mrr_at_1
1753
- value: 0.0
1754
  - type: mrr_at_10
1755
- value: 0.0
1756
  - type: mrr_at_100
1757
- value: 0.0
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  - type: mrr_at_1000
1759
- value: 0.0
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  - type: mrr_at_3
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- value: 0.0
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  - type: mrr_at_5
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- value: 0.0
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  - type: ndcg_at_1
1765
  value: 47.959
1766
  - type: ndcg_at_10
@@ -2003,6 +2005,7 @@ model-index:
2003
  language:
2004
  - en
2005
  license: cc-by-nc-4.0
 
2006
  ---
2007
  ## Introduction
2008
  We present NV-Embed-v2, a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark ([MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard))(as of Aug 30, 2024) with a score of 72.31 across 56 text embedding tasks. It also holds the No. 1 in the retrieval sub-category (a score of 62.65 across 15 tasks) in the leaderboard, which is essential to the development of RAG technology.
@@ -2183,4 +2186,4 @@ cd sentence-transformers
2183
  git checkout v2.7-release
2184
  # Modify L353 in SentenceTransformer.py to **'extra_features["prompt_length"] = tokenized_prompt["input_ids"].shape[-1]'**.
2185
  pip install -e .
2186
- ```
 
1
  ---
2
  tags:
3
  - mteb
4
+ - transformers
5
+ - sentence-transformers
6
  model-index:
7
  - name: NV-Embed-v2
8
  results:
 
91
  - type: map_at_5
92
  value: 61.027
93
  - type: mrr_at_1
94
+ value: 0
95
  - type: mrr_at_10
96
+ value: 0
97
  - type: mrr_at_100
98
+ value: 0
99
  - type: mrr_at_1000
100
+ value: 0
101
  - type: mrr_at_3
102
+ value: 0
103
  - type: mrr_at_5
104
+ value: 0
105
  - type: ndcg_at_1
106
  value: 46.515
107
  - type: ndcg_at_10
 
279
  - type: map_at_5
280
  value: 42.317083333333336
281
  - type: mrr_at_1
282
+ value: 0
283
  - type: mrr_at_10
284
+ value: 0
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  - type: mrr_at_100
286
+ value: 0
287
  - type: mrr_at_1000
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+ value: 0
289
  - type: mrr_at_3
290
+ value: 0
291
  - type: mrr_at_5
292
+ value: 0
293
  - type: ndcg_at_1
294
  value: 38.30616666666667
295
  - type: ndcg_at_10
 
350
  - type: map_at_5
351
  value: 31.955
352
  - type: mrr_at_1
353
+ value: 0
354
  - type: mrr_at_10
355
+ value: 0
356
  - type: mrr_at_100
357
+ value: 0
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  - type: mrr_at_1000
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+ value: 0
360
  - type: mrr_at_3
361
+ value: 0
362
  - type: mrr_at_5
363
+ value: 0
364
  - type: ndcg_at_1
365
  value: 44.104
366
  - type: ndcg_at_10
 
421
  - type: map_at_5
422
  value: 21.062
423
  - type: mrr_at_1
424
+ value: 0
425
  - type: mrr_at_10
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+ value: 0
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  - type: mrr_at_100
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+ value: 0
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  - type: mrr_at_1000
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+ value: 0
431
  - type: mrr_at_3
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+ value: 0
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  - type: mrr_at_5
434
+ value: 0
435
  - type: ndcg_at_1
436
+ value: 66
437
  - type: ndcg_at_10
438
  value: 53.496
439
  - type: ndcg_at_100
 
453
  - type: precision_at_1000
454
  value: 2.5940000000000003
455
  - type: precision_at_3
456
+ value: 61
457
  - type: precision_at_5
458
  value: 54.65
459
  - type: recall_at_1
 
511
  - type: map_at_5
512
  value: 91.262
513
  - type: mrr_at_1
514
+ value: 0
515
  - type: mrr_at_10
516
+ value: 0
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  - type: mrr_at_100
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+ value: 0
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  - type: mrr_at_1000
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+ value: 0
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  - type: mrr_at_3
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+ value: 0
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  - type: mrr_at_5
524
+ value: 0
525
  - type: ndcg_at_1
526
  value: 91.20899999999999
527
  - type: ndcg_at_10
 
582
  - type: map_at_5
583
  value: 55.054
584
  - type: mrr_at_1
585
+ value: 0
586
  - type: mrr_at_10
587
+ value: 0
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  - type: mrr_at_100
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+ value: 0
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  - type: mrr_at_1000
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+ value: 0
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  - type: mrr_at_3
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+ value: 0
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  - type: mrr_at_5
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+ value: 0
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  - type: ndcg_at_1
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  value: 64.815
598
  - type: ndcg_at_10
 
653
  - type: map_at_5
654
  value: 78.935
655
  - type: mrr_at_1
656
+ value: 0
657
  - type: mrr_at_10
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+ value: 0
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  - type: mrr_at_100
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+ value: 0
661
  - type: mrr_at_1000
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+ value: 0
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  - type: mrr_at_3
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+ value: 0
665
  - type: mrr_at_5
666
+ value: 0
667
  - type: ndcg_at_1
668
  value: 89.305
669
  - type: ndcg_at_10
 
735
  type: mteb/msmarco
736
  metrics:
737
  - type: map_at_1
738
+ value: 0
739
  - type: map_at_10
740
  value: 38.342
741
  - type: map_at_100
742
+ value: 0
743
  - type: map_at_1000
744
+ value: 0
745
  - type: map_at_3
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+ value: 0
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  - type: map_at_5
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+ value: 0
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  - type: mrr_at_1
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+ value: 0
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  - type: mrr_at_10
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+ value: 0
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  - type: mrr_at_100
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+ value: 0
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  - type: mrr_at_1000
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+ value: 0
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  - type: mrr_at_3
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+ value: 0
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  - type: mrr_at_5
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+ value: 0
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  - type: ndcg_at_1
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+ value: 0
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  - type: ndcg_at_10
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  value: 45.629999999999995
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  - type: ndcg_at_100
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+ value: 0
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  - type: ndcg_at_1000
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+ value: 0
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  - type: ndcg_at_3
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+ value: 0
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  - type: ndcg_at_5
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+ value: 0
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  - type: precision_at_1
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+ value: 0
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  - type: precision_at_10
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  value: 7.119000000000001
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  - type: precision_at_100
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+ value: 0
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  - type: precision_at_1000
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+ value: 0
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  - type: precision_at_3
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+ value: 0
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  - type: precision_at_5
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+ value: 0
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  - type: recall_at_1
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+ value: 0
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  - type: recall_at_10
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  value: 67.972
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  - type: recall_at_100
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+ value: 0
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  - type: recall_at_1000
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+ value: 0
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  - type: recall_at_3
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+ value: 0
795
  - type: recall_at_5
796
+ value: 0
797
  - type: main_score
798
  value: 45.629999999999995
799
  task:
 
939
  - type: map_at_5
940
  value: 15.171000000000001
941
  - type: mrr_at_1
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+ value: 0
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  - type: mrr_at_10
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+ value: 0
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  - type: mrr_at_100
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+ value: 0
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  - type: mrr_at_1000
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+ value: 0
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  - type: mrr_at_3
950
+ value: 0
951
  - type: mrr_at_5
952
+ value: 0
953
  - type: ndcg_at_1
954
  value: 55.728
955
  - type: ndcg_at_10
 
1010
  - type: map_at_5
1011
  value: 65.364
1012
  - type: mrr_at_1
1013
+ value: 0
1014
  - type: mrr_at_10
1015
+ value: 0
1016
  - type: mrr_at_100
1017
+ value: 0
1018
  - type: mrr_at_1000
1019
+ value: 0
1020
  - type: mrr_at_3
1021
+ value: 0
1022
  - type: mrr_at_5
1023
+ value: 0
1024
  - type: ndcg_at_1
1025
  value: 55.417
1026
  - type: ndcg_at_10
 
1081
  - type: map_at_5
1082
  value: 84.396
1083
  - type: mrr_at_1
1084
+ value: 0
1085
  - type: mrr_at_10
1086
+ value: 0
1087
  - type: mrr_at_100
1088
+ value: 0
1089
  - type: mrr_at_1000
1090
+ value: 0
1091
  - type: mrr_at_3
1092
+ value: 0
1093
  - type: mrr_at_5
1094
+ value: 0
1095
  - type: ndcg_at_1
1096
  value: 82.12
1097
  - type: ndcg_at_10
 
1182
  - type: map_at_5
1183
  value: 11.158
1184
  - type: mrr_at_1
1185
+ value: 0
1186
  - type: mrr_at_10
1187
+ value: 0
1188
  - type: mrr_at_100
1189
+ value: 0
1190
  - type: mrr_at_1000
1191
+ value: 0
1192
  - type: mrr_at_3
1193
+ value: 0
1194
  - type: mrr_at_5
1195
+ value: 0
1196
  - type: ndcg_at_1
1197
  value: 26.3
1198
  - type: ndcg_at_10
 
1475
  - type: map_at_5
1476
  value: 74.74
1477
  - type: mrr_at_1
1478
+ value: 0
1479
  - type: mrr_at_10
1480
+ value: 0
1481
  - type: mrr_at_100
1482
+ value: 0
1483
  - type: mrr_at_1000
1484
+ value: 0
1485
  - type: mrr_at_3
1486
+ value: 0
1487
  - type: mrr_at_5
1488
+ value: 0
1489
  - type: ndcg_at_1
1490
+ value: 66
1491
  - type: ndcg_at_10
1492
  value: 80.12700000000001
1493
  - type: ndcg_at_100
 
1499
  - type: ndcg_at_5
1500
  value: 78.827
1501
  - type: precision_at_1
1502
+ value: 66
1503
  - type: precision_at_10
1504
  value: 10.567
1505
  - type: precision_at_100
 
1517
  - type: recall_at_100
1518
  value: 98.667
1519
  - type: recall_at_1000
1520
+ value: 100
1521
  - type: recall_at_3
1522
  value: 83.322
1523
  - type: recall_at_5
 
1681
  - type: map_at_5
1682
  value: 1.185
1683
  - type: mrr_at_1
1684
+ value: 0
1685
  - type: mrr_at_10
1686
+ value: 0
1687
  - type: mrr_at_100
1688
+ value: 0
1689
  - type: mrr_at_1000
1690
+ value: 0
1691
  - type: mrr_at_3
1692
+ value: 0
1693
  - type: mrr_at_5
1694
+ value: 0
1695
  - type: ndcg_at_1
1696
+ value: 91
1697
  - type: ndcg_at_10
1698
  value: 88.442
1699
  - type: ndcg_at_100
 
1705
  - type: ndcg_at_5
1706
  value: 89.562
1707
  - type: precision_at_1
1708
+ value: 92
1709
  - type: precision_at_10
1710
  value: 92.60000000000001
1711
  - type: precision_at_100
 
1713
  - type: precision_at_1000
1714
  value: 28.222
1715
  - type: precision_at_3
1716
+ value: 94
1717
  - type: precision_at_5
1718
  value: 93.60000000000001
1719
  - type: recall_at_1
 
1752
  - type: map_at_5
1753
  value: 9.49
1754
  - type: mrr_at_1
1755
+ value: 0
1756
  - type: mrr_at_10
1757
+ value: 0
1758
  - type: mrr_at_100
1759
+ value: 0
1760
  - type: mrr_at_1000
1761
+ value: 0
1762
  - type: mrr_at_3
1763
+ value: 0
1764
  - type: mrr_at_5
1765
+ value: 0
1766
  - type: ndcg_at_1
1767
  value: 47.959
1768
  - type: ndcg_at_10
 
2005
  language:
2006
  - en
2007
  license: cc-by-nc-4.0
2008
+ base_model: mistralai/Mistral-7B-v0.1
2009
  ---
2010
  ## Introduction
2011
  We present NV-Embed-v2, a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark ([MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard))(as of Aug 30, 2024) with a score of 72.31 across 56 text embedding tasks. It also holds the No. 1 in the retrieval sub-category (a score of 62.65 across 15 tasks) in the leaderboard, which is essential to the development of RAG technology.
 
2186
  git checkout v2.7-release
2187
  # Modify L353 in SentenceTransformer.py to **'extra_features["prompt_length"] = tokenized_prompt["input_ids"].shape[-1]'**.
2188
  pip install -e .
2189
+ ```