---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- language
- granite
- embeddings
model-index:
- name: ibm-granite/granite-embedding-30m-english
  results:
  - dataset:
      type: mteb/arguana
      name: MTEB ArguaAna
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.31792
    - type: map_at_10
      value: 0.47599
    - type: map_at_100
      value: 0.48425
    - type: map_at_1000
      value: 0.48427
    - type: map_at_3
      value: 0.42757
    - type: map_at_5
      value: 0.45634
    - type: mrr_at_1
      value: 0.32788
    - type: mrr_at_10
      value: 0.47974
    - type: mrr_at_100
      value: 0.48801
    - type: mrr_at_1000
      value: 0.48802
    - type: mrr_at_3
      value: 0.43065
    - type: mrr_at_5
      value: 0.45999
    - type: ndcg_at_1
      value: 0.31792
    - type: ndcg_at_10
      value: 0.56356
    - type: ndcg_at_100
      value: 0.59789
    - type: ndcg_at_1000
      value: 0.59857
    - type: ndcg_at_3
      value: 0.46453
    - type: ndcg_at_5
      value: 0.51623
    - type: precision_at_1
      value: 0.31792
    - type: precision_at_10
      value: 0.08428
    - type: precision_at_100
      value: 0.00991
    - type: precision_at_1000
      value: 0.001
    - type: precision_at_3
      value: 0.19061
    - type: precision_at_5
      value: 0.1394
    - type: recall_at_1
      value: 0.31792
    - type: recall_at_10
      value: 0.84282
    - type: recall_at_100
      value: 0.99075
    - type: recall_at_1000
      value: 0.99644
    - type: recall_at_3
      value: 0.57183
    - type: recall_at_5
      value: 0.69701
  - dataset:
      type: mteb/climate-fever
      name: MTEB ClimateFEVER
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.13189
    - type: map_at_10
      value: 0.21789
    - type: map_at_100
      value: 0.2358
    - type: map_at_1000
      value: 0.23772
    - type: map_at_3
      value: 0.18513
    - type: map_at_5
      value: 0.20212
    - type: mrr_at_1
      value: 0.29837
    - type: mrr_at_10
      value: 0.41376
    - type: mrr_at_100
      value: 0.42282
    - type: mrr_at_1000
      value: 0.42319
    - type: mrr_at_3
      value: 0.38284
    - type: mrr_at_5
      value: 0.40301
    - type: ndcg_at_1
      value: 0.29837
    - type: ndcg_at_10
      value: 0.30263
    - type: ndcg_at_100
      value: 0.37228
    - type: ndcg_at_1000
      value: 0.40677
    - type: ndcg_at_3
      value: 0.25392
    - type: ndcg_at_5
      value: 0.27153
    - type: precision_at_1
      value: 0.29837
    - type: precision_at_10
      value: 0.09179
    - type: precision_at_100
      value: 0.01659
    - type: precision_at_1000
      value: 0.0023
    - type: precision_at_3
      value: 0.18545
    - type: precision_at_5
      value: 0.14241
    - type: recall_at_1
      value: 0.13189
    - type: recall_at_10
      value: 0.35355
    - type: recall_at_100
      value: 0.59255
    - type: recall_at_1000
      value: 0.78637
    - type: recall_at_3
      value: 0.23255
    - type: recall_at_5
      value: 0.28446
  - dataset:
      type: mteb/cqadupstack-android
      name: MTEB CQADupstackAndroidRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.35797
    - type: map_at_10
      value: 0.47793
    - type: map_at_100
      value: 0.49422
    - type: map_at_1000
      value: 0.49546
    - type: map_at_3
      value: 0.44137
    - type: map_at_5
      value: 0.46063
    - type: mrr_at_1
      value: 0.44206
    - type: mrr_at_10
      value: 0.53808
    - type: mrr_at_100
      value: 0.5454
    - type: mrr_at_1000
      value: 0.54578
    - type: mrr_at_3
      value: 0.51431
    - type: mrr_at_5
      value: 0.5284
    - type: ndcg_at_1
      value: 0.44206
    - type: ndcg_at_10
      value: 0.54106
    - type: ndcg_at_100
      value: 0.59335
    - type: ndcg_at_1000
      value: 0.61015
    - type: ndcg_at_3
      value: 0.49365
    - type: ndcg_at_5
      value: 0.51429
    - type: precision_at_1
      value: 0.44206
    - type: precision_at_10
      value: 0.10443
    - type: precision_at_100
      value: 0.01631
    - type: precision_at_1000
      value: 0.00214
    - type: precision_at_3
      value: 0.23653
    - type: precision_at_5
      value: 0.1691
    - type: recall_at_1
      value: 0.35797
    - type: recall_at_10
      value: 0.65182
    - type: recall_at_100
      value: 0.86654
    - type: recall_at_1000
      value: 0.97131
    - type: recall_at_3
      value: 0.51224
    - type: recall_at_5
      value: 0.57219
  - dataset:
      type: mteb/cqadupstack-english
      name: MTEB CQADupstackEnglishRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.32748
    - type: map_at_10
      value: 0.44138
    - type: map_at_100
      value: 0.45565
    - type: map_at_1000
      value: 0.45698
    - type: map_at_3
      value: 0.40916
    - type: map_at_5
      value: 0.42621
    - type: mrr_at_1
      value: 0.41274
    - type: mrr_at_10
      value: 0.5046
    - type: mrr_at_100
      value: 0.5107
    - type: mrr_at_1000
      value: 0.51109
    - type: mrr_at_3
      value: 0.48238
    - type: mrr_at_5
      value: 0.49563
    - type: ndcg_at_1
      value: 0.41274
    - type: ndcg_at_10
      value: 0.50251
    - type: ndcg_at_100
      value: 0.54725
    - type: ndcg_at_1000
      value: 0.56635
    - type: ndcg_at_3
      value: 0.46023
    - type: ndcg_at_5
      value: 0.47883
    - type: precision_at_1
      value: 0.41274
    - type: precision_at_10
      value: 0.09828
    - type: precision_at_100
      value: 0.01573
    - type: precision_at_1000
      value: 0.00202
    - type: precision_at_3
      value: 0.22718
    - type: precision_at_5
      value: 0.16064
    - type: recall_at_1
      value: 0.32748
    - type: recall_at_10
      value: 0.60322
    - type: recall_at_100
      value: 0.79669
    - type: recall_at_1000
      value: 0.9173
    - type: recall_at_3
      value: 0.47523
    - type: recall_at_5
      value: 0.52957
  - dataset:
      type: mteb/cqadupstack-gaming
      name: MTEB CQADupstackGamingRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.41126
    - type: map_at_10
      value: 0.53661
    - type: map_at_100
      value: 0.54588
    - type: map_at_1000
      value: 0.54638
    - type: map_at_3
      value: 0.50389
    - type: map_at_5
      value: 0.52286
    - type: mrr_at_1
      value: 0.47147
    - type: mrr_at_10
      value: 0.5685
    - type: mrr_at_100
      value: 0.57458
    - type: mrr_at_1000
      value: 0.57487
    - type: mrr_at_3
      value: 0.54431
    - type: mrr_at_5
      value: 0.55957
    - type: ndcg_at_1
      value: 0.47147
    - type: ndcg_at_10
      value: 0.59318
    - type: ndcg_at_100
      value: 0.62972
    - type: ndcg_at_1000
      value: 0.64033
    - type: ndcg_at_3
      value: 0.53969
    - type: ndcg_at_5
      value: 0.56743
    - type: precision_at_1
      value: 0.47147
    - type: precision_at_10
      value: 0.09549
    - type: precision_at_100
      value: 0.01224
    - type: precision_at_1000
      value: 0.00135
    - type: precision_at_3
      value: 0.24159
    - type: precision_at_5
      value: 0.16577
    - type: recall_at_1
      value: 0.41126
    - type: recall_at_10
      value: 0.72691
    - type: recall_at_100
      value: 0.88692
    - type: recall_at_1000
      value: 0.96232
    - type: recall_at_3
      value: 0.58374
    - type: recall_at_5
      value: 0.65226
  - dataset:
      type: mteb/cqadupstack-gis
      name: MTEB CQADupstackGisRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.28464
    - type: map_at_10
      value: 0.3828
    - type: map_at_100
      value: 0.39277
    - type: map_at_1000
      value: 0.39355
    - type: map_at_3
      value: 0.35704
    - type: map_at_5
      value: 0.37116
    - type: mrr_at_1
      value: 0.30734
    - type: mrr_at_10
      value: 0.40422
    - type: mrr_at_100
      value: 0.41297
    - type: mrr_at_1000
      value: 0.41355
    - type: mrr_at_3
      value: 0.38136
    - type: mrr_at_5
      value: 0.39362
    - type: ndcg_at_1
      value: 0.30734
    - type: ndcg_at_10
      value: 0.43564
    - type: ndcg_at_100
      value: 0.48419
    - type: ndcg_at_1000
      value: 0.50404
    - type: ndcg_at_3
      value: 0.38672
    - type: ndcg_at_5
      value: 0.40954
    - type: precision_at_1
      value: 0.30734
    - type: precision_at_10
      value: 0.06633
    - type: precision_at_100
      value: 0.00956
    - type: precision_at_1000
      value: 0.00116
    - type: precision_at_3
      value: 0.16497
    - type: precision_at_5
      value: 0.11254
    - type: recall_at_1
      value: 0.28464
    - type: recall_at_10
      value: 0.57621
    - type: recall_at_100
      value: 0.7966
    - type: recall_at_1000
      value: 0.94633
    - type: recall_at_3
      value: 0.44588
    - type: recall_at_5
      value: 0.50031
  - dataset:
      type: mteb/cqadupstack-mathematica
      name: MTEB CQADupstackMathematicaRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.18119
    - type: map_at_10
      value: 0.27055
    - type: map_at_100
      value: 0.28461
    - type: map_at_1000
      value: 0.28577
    - type: map_at_3
      value: 0.24341
    - type: map_at_5
      value: 0.25861
    - type: mrr_at_1
      value: 0.22886
    - type: mrr_at_10
      value: 0.32234
    - type: mrr_at_100
      value: 0.3328
    - type: mrr_at_1000
      value: 0.3334
    - type: mrr_at_3
      value: 0.29664
    - type: mrr_at_5
      value: 0.31107
    - type: ndcg_at_1
      value: 0.22886
    - type: ndcg_at_10
      value: 0.32749
    - type: ndcg_at_100
      value: 0.39095
    - type: ndcg_at_1000
      value: 0.41656
    - type: ndcg_at_3
      value: 0.27864
    - type: ndcg_at_5
      value: 0.30177
    - type: precision_at_1
      value: 0.22886
    - type: precision_at_10
      value: 0.06169
    - type: precision_at_100
      value: 0.0107
    - type: precision_at_1000
      value: 0.00143
    - type: precision_at_3
      value: 0.13682
    - type: precision_at_5
      value: 0.0995
    - type: recall_at_1
      value: 0.18119
    - type: recall_at_10
      value: 0.44983
    - type: recall_at_100
      value: 0.72396
    - type: recall_at_1000
      value: 0.90223
    - type: recall_at_3
      value: 0.31633
    - type: recall_at_5
      value: 0.37532
  - dataset:
      type: mteb/cqadupstack-physics
      name: MTEB CQADupstackPhysicsRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.30517
    - type: map_at_10
      value: 0.42031
    - type: map_at_100
      value: 0.43415
    - type: map_at_1000
      value: 0.43525
    - type: map_at_3
      value: 0.38443
    - type: map_at_5
      value: 0.40685
    - type: mrr_at_1
      value: 0.38114
    - type: mrr_at_10
      value: 0.47783
    - type: mrr_at_100
      value: 0.48647
    - type: mrr_at_1000
      value: 0.48688
    - type: mrr_at_3
      value: 0.45172
    - type: mrr_at_5
      value: 0.46817
    - type: ndcg_at_1
      value: 0.38114
    - type: ndcg_at_10
      value: 0.4834
    - type: ndcg_at_100
      value: 0.53861
    - type: ndcg_at_1000
      value: 0.55701
    - type: ndcg_at_3
      value: 0.42986
    - type: ndcg_at_5
      value: 0.45893
    - type: precision_at_1
      value: 0.38114
    - type: precision_at_10
      value: 0.08893
    - type: precision_at_100
      value: 0.01375
    - type: precision_at_1000
      value: 0.00172
    - type: precision_at_3
      value: 0.20821
    - type: precision_at_5
      value: 0.15034
    - type: recall_at_1
      value: 0.30517
    - type: recall_at_10
      value: 0.61332
    - type: recall_at_100
      value: 0.84051
    - type: recall_at_1000
      value: 0.95826
    - type: recall_at_3
      value: 0.46015
    - type: recall_at_5
      value: 0.53801
  - dataset:
      type: mteb/cqadupstack-programmers
      name: MTEB CQADupstackProgrammersRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.27396
    - type: map_at_10
      value: 0.38043
    - type: map_at_100
      value: 0.39341
    - type: map_at_1000
      value: 0.39454
    - type: map_at_3
      value: 0.34783
    - type: map_at_5
      value: 0.3663
    - type: mrr_at_1
      value: 0.34247
    - type: mrr_at_10
      value: 0.43681
    - type: mrr_at_100
      value: 0.4451
    - type: mrr_at_1000
      value: 0.44569
    - type: mrr_at_3
      value: 0.41172
    - type: mrr_at_5
      value: 0.42702
    - type: ndcg_at_1
      value: 0.34247
    - type: ndcg_at_10
      value: 0.44065
    - type: ndcg_at_100
      value: 0.49434
    - type: ndcg_at_1000
      value: 0.51682
    - type: ndcg_at_3
      value: 0.38976
    - type: ndcg_at_5
      value: 0.41332
    - type: precision_at_1
      value: 0.34247
    - type: precision_at_10
      value: 0.08059
    - type: precision_at_100
      value: 0.01258
    - type: precision_at_1000
      value: 0.00162
    - type: precision_at_3
      value: 0.1876
    - type: precision_at_5
      value: 0.13333
    - type: recall_at_1
      value: 0.27396
    - type: recall_at_10
      value: 0.56481
    - type: recall_at_100
      value: 0.79012
    - type: recall_at_1000
      value: 0.94182
    - type: recall_at_3
      value: 0.41785
    - type: recall_at_5
      value: 0.48303
  - dataset:
      type: mteb/cqadupstack-stats
      name: MTEB CQADupstackStatsRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.25728
    - type: map_at_10
      value: 0.33903
    - type: map_at_100
      value: 0.34853
    - type: map_at_1000
      value: 0.34944
    - type: map_at_3
      value: 0.31268
    - type: map_at_5
      value: 0.32596
    - type: mrr_at_1
      value: 0.29141
    - type: mrr_at_10
      value: 0.36739
    - type: mrr_at_100
      value: 0.37545
    - type: mrr_at_1000
      value: 0.37608
    - type: mrr_at_3
      value: 0.34407
    - type: mrr_at_5
      value: 0.3568
    - type: ndcg_at_1
      value: 0.29141
    - type: ndcg_at_10
      value: 0.38596
    - type: ndcg_at_100
      value: 0.43375
    - type: ndcg_at_1000
      value: 0.45562
    - type: ndcg_at_3
      value: 0.33861
    - type: ndcg_at_5
      value: 0.35887
    - type: precision_at_1
      value: 0.29141
    - type: precision_at_10
      value: 0.06334
    - type: precision_at_100
      value: 0.00952
    - type: precision_at_1000
      value: 0.00121
    - type: precision_at_3
      value: 0.14826
    - type: precision_at_5
      value: 0.10429
    - type: recall_at_1
      value: 0.25728
    - type: recall_at_10
      value: 0.50121
    - type: recall_at_100
      value: 0.72382
    - type: recall_at_1000
      value: 0.88306
    - type: recall_at_3
      value: 0.36638
    - type: recall_at_5
      value: 0.41689
  - dataset:
      type: mteb/cqadupstack-tex
      name: MTEB CQADupstackTexRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.19911
    - type: map_at_10
      value: 0.2856
    - type: map_at_100
      value: 0.29785
    - type: map_at_1000
      value: 0.29911
    - type: map_at_3
      value: 0.25875
    - type: map_at_5
      value: 0.2741
    - type: mrr_at_1
      value: 0.24054
    - type: mrr_at_10
      value: 0.32483
    - type: mrr_at_100
      value: 0.33464
    - type: mrr_at_1000
      value: 0.33534
    - type: mrr_at_3
      value: 0.30162
    - type: mrr_at_5
      value: 0.31506
    - type: ndcg_at_1
      value: 0.24054
    - type: ndcg_at_10
      value: 0.33723
    - type: ndcg_at_100
      value: 0.39362
    - type: ndcg_at_1000
      value: 0.42065
    - type: ndcg_at_3
      value: 0.29116
    - type: ndcg_at_5
      value: 0.31299
    - type: precision_at_1
      value: 0.24054
    - type: precision_at_10
      value: 0.06194
    - type: precision_at_100
      value: 0.01058
    - type: precision_at_1000
      value: 0.00148
    - type: precision_at_3
      value: 0.13914
    - type: precision_at_5
      value: 0.10076
    - type: recall_at_1
      value: 0.19911
    - type: recall_at_10
      value: 0.45183
    - type: recall_at_100
      value: 0.7025
    - type: recall_at_1000
      value: 0.89222
    - type: recall_at_3
      value: 0.32195
    - type: recall_at_5
      value: 0.37852
  - dataset:
      type: mteb/cqadupstack-unix
      name: MTEB CQADupstackUnixRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.29819
    - type: map_at_10
      value: 0.40073
    - type: map_at_100
      value: 0.41289
    - type: map_at_1000
      value: 0.41375
    - type: map_at_3
      value: 0.36572
    - type: map_at_5
      value: 0.38386
    - type: mrr_at_1
      value: 0.35168
    - type: mrr_at_10
      value: 0.44381
    - type: mrr_at_100
      value: 0.45191
    - type: mrr_at_1000
      value: 0.45234
    - type: mrr_at_3
      value: 0.41402
    - type: mrr_at_5
      value: 0.43039
    - type: ndcg_at_1
      value: 0.35168
    - type: ndcg_at_10
      value: 0.46071
    - type: ndcg_at_100
      value: 0.51351
    - type: ndcg_at_1000
      value: 0.5317
    - type: ndcg_at_3
      value: 0.39972
    - type: ndcg_at_5
      value: 0.42586
    - type: precision_at_1
      value: 0.35168
    - type: precision_at_10
      value: 0.07985
    - type: precision_at_100
      value: 0.01185
    - type: precision_at_1000
      value: 0.00144
    - type: precision_at_3
      value: 0.18221
    - type: precision_at_5
      value: 0.12892
    - type: recall_at_1
      value: 0.29819
    - type: recall_at_10
      value: 0.60075
    - type: recall_at_100
      value: 0.82771
    - type: recall_at_1000
      value: 0.95219
    - type: recall_at_3
      value: 0.43245
    - type: recall_at_5
      value: 0.49931
  - dataset:
      type: mteb/cqadupstack-webmasters
      name: MTEB CQADupstackWebmastersRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.28409
    - type: map_at_10
      value: 0.37621
    - type: map_at_100
      value: 0.39233
    - type: map_at_1000
      value: 0.39471
    - type: map_at_3
      value: 0.34337
    - type: map_at_5
      value: 0.35985
    - type: mrr_at_1
      value: 0.33794
    - type: mrr_at_10
      value: 0.42349
    - type: mrr_at_100
      value: 0.43196
    - type: mrr_at_1000
      value: 0.43237
    - type: mrr_at_3
      value: 0.39526
    - type: mrr_at_5
      value: 0.41087
    - type: ndcg_at_1
      value: 0.33794
    - type: ndcg_at_10
      value: 0.43832
    - type: ndcg_at_100
      value: 0.49514
    - type: ndcg_at_1000
      value: 0.51742
    - type: ndcg_at_3
      value: 0.38442
    - type: ndcg_at_5
      value: 0.40737
    - type: precision_at_1
      value: 0.33794
    - type: precision_at_10
      value: 0.08597
    - type: precision_at_100
      value: 0.01652
    - type: precision_at_1000
      value: 0.00251
    - type: precision_at_3
      value: 0.17787
    - type: precision_at_5
      value: 0.13241
    - type: recall_at_1
      value: 0.28409
    - type: recall_at_10
      value: 0.55388
    - type: recall_at_100
      value: 0.81517
    - type: recall_at_1000
      value: 0.95038
    - type: recall_at_3
      value: 0.40133
    - type: recall_at_5
      value: 0.45913
  - dataset:
      type: mteb/cqadupstack-wordpress
      name: MTEB CQADupstackWordpressRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.24067
    - type: map_at_10
      value: 0.32184
    - type: map_at_100
      value: 0.33357
    - type: map_at_1000
      value: 0.33458
    - type: map_at_3
      value: 0.29492
    - type: map_at_5
      value: 0.3111
    - type: mrr_at_1
      value: 0.26248
    - type: mrr_at_10
      value: 0.34149
    - type: mrr_at_100
      value: 0.35189
    - type: mrr_at_1000
      value: 0.35251
    - type: mrr_at_3
      value: 0.31639
    - type: mrr_at_5
      value: 0.33182
    - type: ndcg_at_1
      value: 0.26248
    - type: ndcg_at_10
      value: 0.36889
    - type: ndcg_at_100
      value: 0.42426
    - type: ndcg_at_1000
      value: 0.44745
    - type: ndcg_at_3
      value: 0.31799
    - type: ndcg_at_5
      value: 0.34563
    - type: precision_at_1
      value: 0.26248
    - type: precision_at_10
      value: 0.05712
    - type: precision_at_100
      value: 0.00915
    - type: precision_at_1000
      value: 0.00123
    - type: precision_at_3
      value: 0.13309
    - type: precision_at_5
      value: 0.09649
    - type: recall_at_1
      value: 0.24067
    - type: recall_at_10
      value: 0.49344
    - type: recall_at_100
      value: 0.7412
    - type: recall_at_1000
      value: 0.91276
    - type: recall_at_3
      value: 0.36272
    - type: recall_at_5
      value: 0.4277
  - dataset:
      type: mteb/dbpedia
      name: MTEB DBPedia
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.08651
    - type: map_at_10
      value: 0.17628
    - type: map_at_100
      value: 0.23354
    - type: map_at_1000
      value: 0.24827
    - type: map_at_3
      value: 0.1351
    - type: map_at_5
      value: 0.15468
    - type: mrr_at_1
      value: 0.645
    - type: mrr_at_10
      value: 0.71989
    - type: mrr_at_100
      value: 0.72332
    - type: mrr_at_1000
      value: 0.72346
    - type: mrr_at_3
      value: 0.7025
    - type: mrr_at_5
      value: 0.71275
    - type: ndcg_at_1
      value: 0.51375
    - type: ndcg_at_10
      value: 0.3596
    - type: ndcg_at_100
      value: 0.39878
    - type: ndcg_at_1000
      value: 0.47931
    - type: ndcg_at_3
      value: 0.41275
    - type: ndcg_at_5
      value: 0.38297
    - type: precision_at_1
      value: 0.645
    - type: precision_at_10
      value: 0.2745
    - type: precision_at_100
      value: 0.08405
    - type: precision_at_1000
      value: 0.01923
    - type: precision_at_3
      value: 0.44417
    - type: precision_at_5
      value: 0.366
    - type: recall_at_1
      value: 0.08651
    - type: recall_at_10
      value: 0.22416
    - type: recall_at_100
      value: 0.46381
    - type: recall_at_1000
      value: 0.71557
    - type: recall_at_3
      value: 0.14847
    - type: recall_at_5
      value: 0.1804
  - dataset:
      type: mteb/fever
      name: MTEB FEVER
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.73211
    - type: map_at_10
      value: 0.81463
    - type: map_at_100
      value: 0.81622
    - type: map_at_1000
      value: 0.81634
    - type: map_at_3
      value: 0.805
    - type: map_at_5
      value: 0.81134
    - type: mrr_at_1
      value: 0.79088
    - type: mrr_at_10
      value: 0.86943
    - type: mrr_at_100
      value: 0.87017
    - type: mrr_at_1000
      value: 0.87018
    - type: mrr_at_3
      value: 0.86154
    - type: mrr_at_5
      value: 0.867
    - type: ndcg_at_1
      value: 0.79088
    - type: ndcg_at_10
      value: 0.85528
    - type: ndcg_at_100
      value: 0.86134
    - type: ndcg_at_1000
      value: 0.86367
    - type: ndcg_at_3
      value: 0.83943
    - type: ndcg_at_5
      value: 0.84878
    - type: precision_at_1
      value: 0.79088
    - type: precision_at_10
      value: 0.10132
    - type: precision_at_100
      value: 0.01055
    - type: precision_at_1000
      value: 0.00109
    - type: precision_at_3
      value: 0.31963
    - type: precision_at_5
      value: 0.19769
    - type: recall_at_1
      value: 0.73211
    - type: recall_at_10
      value: 0.92797
    - type: recall_at_100
      value: 0.95263
    - type: recall_at_1000
      value: 0.96738
    - type: recall_at_3
      value: 0.88328
    - type: recall_at_5
      value: 0.90821
  - dataset:
      type: mteb/fiqa
      name: MTEB FiQA2018
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.18311
    - type: map_at_10
      value: 0.29201
    - type: map_at_100
      value: 0.3093
    - type: map_at_1000
      value: 0.31116
    - type: map_at_3
      value: 0.24778
    - type: map_at_5
      value: 0.27453
    - type: mrr_at_1
      value: 0.35494
    - type: mrr_at_10
      value: 0.44489
    - type: mrr_at_100
      value: 0.4532
    - type: mrr_at_1000
      value: 0.45369
    - type: mrr_at_3
      value: 0.41667
    - type: mrr_at_5
      value: 0.43418
    - type: ndcg_at_1
      value: 0.35494
    - type: ndcg_at_10
      value: 0.36868
    - type: ndcg_at_100
      value: 0.43463
    - type: ndcg_at_1000
      value: 0.46766
    - type: ndcg_at_3
      value: 0.32305
    - type: ndcg_at_5
      value: 0.34332
    - type: precision_at_1
      value: 0.35494
    - type: precision_at_10
      value: 0.10324
    - type: precision_at_100
      value: 0.01707
    - type: precision_at_1000
      value: 0.00229
    - type: precision_at_3
      value: 0.21142
    - type: precision_at_5
      value: 0.16327
    - type: recall_at_1
      value: 0.18311
    - type: recall_at_10
      value: 0.43881
    - type: recall_at_100
      value: 0.68593
    - type: recall_at_1000
      value: 0.8855
    - type: recall_at_3
      value: 0.28824
    - type: recall_at_5
      value: 0.36178
  - dataset:
      type: mteb/hotpotqa
      name: MTEB HotpotQA
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.36766
    - type: map_at_10
      value: 0.53639
    - type: map_at_100
      value: 0.54532
    - type: map_at_1000
      value: 0.54608
    - type: map_at_3
      value: 0.50427
    - type: map_at_5
      value: 0.5245
    - type: mrr_at_1
      value: 0.73531
    - type: mrr_at_10
      value: 0.80104
    - type: mrr_at_100
      value: 0.80341
    - type: mrr_at_1000
      value: 0.80351
    - type: mrr_at_3
      value: 0.78949
    - type: mrr_at_5
      value: 0.79729
    - type: ndcg_at_1
      value: 0.73531
    - type: ndcg_at_10
      value: 0.62918
    - type: ndcg_at_100
      value: 0.66056
    - type: ndcg_at_1000
      value: 0.67554
    - type: ndcg_at_3
      value: 0.58247
    - type: ndcg_at_5
      value: 0.60905
    - type: precision_at_1
      value: 0.73531
    - type: precision_at_10
      value: 0.1302
    - type: precision_at_100
      value: 0.01546
    - type: precision_at_1000
      value: 0.00175
    - type: precision_at_3
      value: 0.36556
    - type: precision_at_5
      value: 0.24032
    - type: recall_at_1
      value: 0.36766
    - type: recall_at_10
      value: 0.65098
    - type: recall_at_100
      value: 0.77306
    - type: recall_at_1000
      value: 0.87252
    - type: recall_at_3
      value: 0.54835
    - type: recall_at_5
      value: 0.60081
  - dataset:
      type: mteb/msmarco
      name: MTEB MSMARCO
      config: default
      split: dev
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.14654
    - type: map_at_10
      value: 0.2472
    - type: map_at_100
      value: 0.25994
    - type: map_at_1000
      value: 0.26067
    - type: map_at_3
      value: 0.21234
    - type: map_at_5
      value: 0.2319
    - type: mrr_at_1
      value: 0.15086
    - type: mrr_at_10
      value: 0.25184
    - type: mrr_at_100
      value: 0.26422
    - type: mrr_at_1000
      value: 0.26489
    - type: mrr_at_3
      value: 0.21731
    - type: mrr_at_5
      value: 0.23674
    - type: ndcg_at_1
      value: 0.15086
    - type: ndcg_at_10
      value: 0.30711
    - type: ndcg_at_100
      value: 0.37221
    - type: ndcg_at_1000
      value: 0.39133
    - type: ndcg_at_3
      value: 0.23567
    - type: ndcg_at_5
      value: 0.27066
    - type: precision_at_1
      value: 0.15086
    - type: precision_at_10
      value: 0.05132
    - type: precision_at_100
      value: 0.00845
    - type: precision_at_1000
      value: 0.00101
    - type: precision_at_3
      value: 0.10277
    - type: precision_at_5
      value: 0.07923
    - type: recall_at_1
      value: 0.14654
    - type: recall_at_10
      value: 0.49341
    - type: recall_at_100
      value: 0.80224
    - type: recall_at_1000
      value: 0.95037
    - type: recall_at_3
      value: 0.29862
    - type: recall_at_5
      value: 0.38274
  - dataset:
      type: mteb/nfcorpus
      name: MTEB NFCorpus
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.05452
    - type: map_at_10
      value: 0.12758
    - type: map_at_100
      value: 0.1593
    - type: map_at_1000
      value: 0.17422
    - type: map_at_3
      value: 0.0945
    - type: map_at_5
      value: 0.1092
    - type: mrr_at_1
      value: 0.43963
    - type: mrr_at_10
      value: 0.53237
    - type: mrr_at_100
      value: 0.53777
    - type: mrr_at_1000
      value: 0.53822
    - type: mrr_at_3
      value: 0.51445
    - type: mrr_at_5
      value: 0.52466
    - type: ndcg_at_1
      value: 0.41486
    - type: ndcg_at_10
      value: 0.33737
    - type: ndcg_at_100
      value: 0.30886
    - type: ndcg_at_1000
      value: 0.40018
    - type: ndcg_at_3
      value: 0.39324
    - type: ndcg_at_5
      value: 0.36949
    - type: precision_at_1
      value: 0.43344
    - type: precision_at_10
      value: 0.24799
    - type: precision_at_100
      value: 0.07895
    - type: precision_at_1000
      value: 0.02091
    - type: precision_at_3
      value: 0.37152
    - type: precision_at_5
      value: 0.31703
    - type: recall_at_1
      value: 0.05452
    - type: recall_at_10
      value: 0.1712
    - type: recall_at_100
      value: 0.30719
    - type: recall_at_1000
      value: 0.62766
    - type: recall_at_3
      value: 0.10733
    - type: recall_at_5
      value: 0.13553
  - dataset:
      type: mteb/nq
      name: MTEB NQ
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.29022
    - type: map_at_10
      value: 0.4373
    - type: map_at_100
      value: 0.44849
    - type: map_at_1000
      value: 0.44877
    - type: map_at_3
      value: 0.39045
    - type: map_at_5
      value: 0.4186
    - type: mrr_at_1
      value: 0.32793
    - type: mrr_at_10
      value: 0.46243
    - type: mrr_at_100
      value: 0.47083
    - type: mrr_at_1000
      value: 0.47101
    - type: mrr_at_3
      value: 0.42261
    - type: mrr_at_5
      value: 0.44775
    - type: ndcg_at_1
      value: 0.32793
    - type: ndcg_at_10
      value: 0.51631
    - type: ndcg_at_100
      value: 0.56287
    - type: ndcg_at_1000
      value: 0.56949
    - type: ndcg_at_3
      value: 0.42782
    - type: ndcg_at_5
      value: 0.47554
    - type: precision_at_1
      value: 0.32793
    - type: precision_at_10
      value: 0.08737
    - type: precision_at_100
      value: 0.01134
    - type: precision_at_1000
      value: 0.0012
    - type: precision_at_3
      value: 0.19583
    - type: precision_at_5
      value: 0.14484
    - type: recall_at_1
      value: 0.29022
    - type: recall_at_10
      value: 0.73325
    - type: recall_at_100
      value: 0.93455
    - type: recall_at_1000
      value: 0.98414
    - type: recall_at_3
      value: 0.50406
    - type: recall_at_5
      value: 0.6145
  - dataset:
      type: mteb/quora
      name: MTEB QuoraRetrieval
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.68941
    - type: map_at_10
      value: 0.82641
    - type: map_at_100
      value: 0.83317
    - type: map_at_1000
      value: 0.83337
    - type: map_at_3
      value: 0.79604
    - type: map_at_5
      value: 0.81525
    - type: mrr_at_1
      value: 0.7935
    - type: mrr_at_10
      value: 0.85969
    - type: mrr_at_100
      value: 0.86094
    - type: mrr_at_1000
      value: 0.86095
    - type: mrr_at_3
      value: 0.84852
    - type: mrr_at_5
      value: 0.85627
    - type: ndcg_at_1
      value: 0.7936
    - type: ndcg_at_10
      value: 0.86687
    - type: ndcg_at_100
      value: 0.88094
    - type: ndcg_at_1000
      value: 0.88243
    - type: ndcg_at_3
      value: 0.83538
    - type: ndcg_at_5
      value: 0.85308
    - type: precision_at_1
      value: 0.7936
    - type: precision_at_10
      value: 0.13145
    - type: precision_at_100
      value: 0.01517
    - type: precision_at_1000
      value: 0.00156
    - type: precision_at_3
      value: 0.36353
    - type: precision_at_5
      value: 0.24044
    - type: recall_at_1
      value: 0.68941
    - type: recall_at_10
      value: 0.94407
    - type: recall_at_100
      value: 0.99226
    - type: recall_at_1000
      value: 0.99958
    - type: recall_at_3
      value: 0.85502
    - type: recall_at_5
      value: 0.90372
  - dataset:
      type: mteb/scidocs
      name: MTEB SCIDOCS
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.04988
    - type: map_at_10
      value: 0.13553
    - type: map_at_100
      value: 0.16136
    - type: map_at_1000
      value: 0.16512
    - type: map_at_3
      value: 0.09439
    - type: map_at_5
      value: 0.1146
    - type: mrr_at_1
      value: 0.246
    - type: mrr_at_10
      value: 0.36792
    - type: mrr_at_100
      value: 0.37973
    - type: mrr_at_1000
      value: 0.38011
    - type: mrr_at_3
      value: 0.33117
    - type: mrr_at_5
      value: 0.35172
    - type: ndcg_at_1
      value: 0.246
    - type: ndcg_at_10
      value: 0.22542
    - type: ndcg_at_100
      value: 0.32326
    - type: ndcg_at_1000
      value: 0.3828
    - type: ndcg_at_3
      value: 0.20896
    - type: ndcg_at_5
      value: 0.18497
    - type: precision_at_1
      value: 0.246
    - type: precision_at_10
      value: 0.1194
    - type: precision_at_100
      value: 0.02616
    - type: precision_at_1000
      value: 0.00404
    - type: precision_at_3
      value: 0.198
    - type: precision_at_5
      value: 0.1654
    - type: recall_at_1
      value: 0.04988
    - type: recall_at_10
      value: 0.24212
    - type: recall_at_100
      value: 0.53105
    - type: recall_at_1000
      value: 0.82022
    - type: recall_at_3
      value: 0.12047
    - type: recall_at_5
      value: 0.16777
  - dataset:
      type: mteb/scifact
      name: MTEB SciFact
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.56578
    - type: map_at_10
      value: 0.66725
    - type: map_at_100
      value: 0.67379
    - type: map_at_1000
      value: 0.674
    - type: map_at_3
      value: 0.63416
    - type: map_at_5
      value: 0.6577
    - type: mrr_at_1
      value: 0.59333
    - type: mrr_at_10
      value: 0.67533
    - type: mrr_at_100
      value: 0.68062
    - type: mrr_at_1000
      value: 0.68082
    - type: mrr_at_3
      value: 0.64944
    - type: mrr_at_5
      value: 0.66928
    - type: ndcg_at_1
      value: 0.59333
    - type: ndcg_at_10
      value: 0.7127
    - type: ndcg_at_100
      value: 0.73889
    - type: ndcg_at_1000
      value: 0.7441
    - type: ndcg_at_3
      value: 0.65793
    - type: ndcg_at_5
      value: 0.69429
    - type: precision_at_1
      value: 0.59333
    - type: precision_at_10
      value: 0.096
    - type: precision_at_100
      value: 0.01087
    - type: precision_at_1000
      value: 0.00113
    - type: precision_at_3
      value: 0.25556
    - type: precision_at_5
      value: 0.17667
    - type: recall_at_1
      value: 0.56578
    - type: recall_at_10
      value: 0.842
    - type: recall_at_100
      value: 0.95667
    - type: recall_at_1000
      value: 0.99667
    - type: recall_at_3
      value: 0.70072
    - type: recall_at_5
      value: 0.79011
  - dataset:
      type: mteb/touche2020
      name: MTEB Touche2020
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.01976
    - type: map_at_10
      value: 0.09688
    - type: map_at_100
      value: 0.15117
    - type: map_at_1000
      value: 0.16769
    - type: map_at_3
      value: 0.04589
    - type: map_at_5
      value: 0.06556
    - type: mrr_at_1
      value: 0.26531
    - type: mrr_at_10
      value: 0.43863
    - type: mrr_at_100
      value: 0.44767
    - type: mrr_at_1000
      value: 0.44767
    - type: mrr_at_3
      value: 0.39116
    - type: mrr_at_5
      value: 0.41156
    - type: ndcg_at_1
      value: 0.23469
    - type: ndcg_at_10
      value: 0.24029
    - type: ndcg_at_100
      value: 0.34425
    - type: ndcg_at_1000
      value: 0.46907
    - type: ndcg_at_3
      value: 0.25522
    - type: ndcg_at_5
      value: 0.24333
    - type: precision_at_1
      value: 0.26531
    - type: precision_at_10
      value: 0.22449
    - type: precision_at_100
      value: 0.07122
    - type: precision_at_1000
      value: 0.01527
    - type: precision_at_3
      value: 0.27891
    - type: precision_at_5
      value: 0.25714
    - type: recall_at_1
      value: 0.01976
    - type: recall_at_10
      value: 0.16633
    - type: recall_at_100
      value: 0.4561
    - type: recall_at_1000
      value: 0.82481
    - type: recall_at_3
      value: 0.06101
    - type: recall_at_5
      value: 0.0968
  - dataset:
      type: mteb/trec-covid
      name: MTEB TRECCOVID
      config: default
      split: test
    task:
      type: Retrieval
    metrics:
    - type: map_at_1
      value: 0.00211
    - type: map_at_10
      value: 0.01526
    - type: map_at_100
      value: 0.08863
    - type: map_at_1000
      value: 0.23162
    - type: map_at_3
      value: 0.00555
    - type: map_at_5
      value: 0.00873
    - type: mrr_at_1
      value: 0.76
    - type: mrr_at_10
      value: 0.8485
    - type: mrr_at_100
      value: 0.8485
    - type: mrr_at_1000
      value: 0.8485
    - type: mrr_at_3
      value: 0.84
    - type: mrr_at_5
      value: 0.844
    - type: ndcg_at_1
      value: 0.7
    - type: ndcg_at_10
      value: 0.63098
    - type: ndcg_at_100
      value: 0.49847
    - type: ndcg_at_1000
      value: 0.48395
    - type: ndcg_at_3
      value: 0.68704
    - type: ndcg_at_5
      value: 0.67533
    - type: precision_at_1
      value: 0.76
    - type: precision_at_10
      value: 0.66
    - type: precision_at_100
      value: 0.5134
    - type: precision_at_1000
      value: 0.2168
    - type: precision_at_3
      value: 0.72667
    - type: precision_at_5
      value: 0.716
    - type: recall_at_1
      value: 0.00211
    - type: recall_at_10
      value: 0.01748
    - type: recall_at_100
      value: 0.12448
    - type: recall_at_1000
      value: 0.46795
    - type: recall_at_3
      value: 0.00593
    - type: recall_at_5
      value: 0.00962
pipeline_tag: sentence-similarity
---
# Granite-Embedding-30m-English

**Model Summary:**
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.

- **Developers:** Granite Embedding Team, IBM
- **GitHub Repository:** 
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** 
- **Release Date**: December 18th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

**Supported Languages:** 
English.

**Intended use:** 
The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.

**Usage with Sentence Transformers:** 
The model is compatible with SentenceTransformer library and is very easy to use:

First, install the sentence transformers library
```shell
pip install sentence_transformers
```

The model can then be used to encode pairs of text and find the similarity between their representations

```python
from sentence_transformers import SentenceTransformer, util

model_path = "ibm-granite/granite-embedding-30m-english"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)

input_queries = [
    ' Who made the song My achy breaky heart? ',
    'summit define'
    ]

input_passages = [
    "Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
    "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
    ]

# encode queries and passages
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)

# calculate cosine similarity
print(util.cos_sim(query_embeddings, passage_embeddings))
```

**Usage with Huggingface Transformers:** 
This is a simple example of how to use the Granite-Embedding-30m-English model with the Transformers library and PyTorch.

First, install the required libraries
```shell
pip install transformers torch
```

The model can then be used to encode pairs of text

```python
import torch
from transformers import AutoModel, AutoTokenizer

model_path = "ibm-granite/granite-embedding-30m-english"

# Load the model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()

input_queries = [
    ' Who made the song My achy breaky heart? ',
    'summit define'
    ]

# tokenize inputs
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')

# encode queries
with torch.no_grad():
    # Queries
    model_output = model(**tokenized_queries)
    # Perform pooling. granite-embedding-30m-english uses CLS Pooling
    query_embeddings = model_output[0][:, 0]

# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)

```
**Evaluation:**

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.

| Model                           | Paramters (M)| Embedding Dimension |  MTEB Retrieval (15) | MTEB Retrieval (14) | CoIR (10) | Retrieval Time (seconds/query)|
|---------------------------------|-------------:|--------------------:|--------------------:|---------------------:|----------:|------------------------------:|
|granite-embedding-30m-english    |30            |384                  |49.1                 |50.4                  |47.0        | 0.16                          |


**Model Architecture:**
Granite-Embedding-30m-English is based on an encoder-only RoBERTa like transformer architecture, trained internally at IBM Research.

| Model                     | granite-embedding-30m-english | granite-embedding-125m-english    | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual |
| :---------                | :-------:| :--------:   | :-----:| :-----:|
| Embedding size            | **384**  | 768          | 384    | 768    |
| Number of layers          | **6**    | 12           | 6      | 12     |
| Number of attention heads | **12**   | 12           | 12     | 12     |
| Intermediate size         | **1536** | 3072         | 1536   | 3072   |
| Activation Function       | **GeLU** | GeLU         | GeLU   | GeLU   |
| Vocabulary Size           | **50265**| 50265        | 250002 | 250002 |
| Max. Sequence Length      | **512**  | 512          | 512    | 512    |
| # Parameters              | **30M**  | 125M         | 107M   | 278M   |


**Training Data:**
Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below:

| **Dataset**                                        | **Num. Pairs** | 
|----------------------------------------------------|:---------------:|
| SPECTER citation triplets                          | 684,100         | 
| Stack Exchange Duplicate questions (titles)        | 304,525         | 
| Stack Exchange Duplicate questions (bodies)        | 250,519         | 
| Stack Exchange Duplicate questions (titles+bodies) | 250,460         | 
| Natural Questions (NQ)                             | 100,231         | 
| SQuAD2.0                                           | 87,599          | 
| PAQ (Question, Answer) pairs                       | 64,371,441       | 
| Stack Exchange (Title, Answer) pairs               | 4,067,139        | 
| Stack Exchange (Title, Body) pairs                 | 23,978,013       | 
| Stack Exchange (Title+Body, Answer) pairs          | 187,195         | 
| S2ORC Citation pairs (Titles)                      | 52,603,982       | 
| S2ORC (Title, Abstract)                            | 41,769,185       | 
| S2ORC (Citations, abstracts)                       | 52,603,982       | 
| WikiAnswers Duplicate question pairs               | 77,427,422       | 
| SearchQA                                           | 582,261         | 
| HotpotQA                                           | 85,000          | 
| Fever                                              | 109,810         | 
| Arxiv                                              | 2,358,545        | 
| Wikipedia                                          | 20,745,403       | 
| PubMed                                             | 20,000,000       | 
| Miracl En Pairs                                    | 9,016           | 
| DBPedia Title-Body Pairs                           | 4,635,922        | 
| Synthetic: Query-Wikipedia Passage                 | 1,879,093        | 
| Synthetic: Fact Verification                       | 9,888           | 
| IBM Internal Triples                               | 40,290          | 
| IBM Internal Title-Body Pairs                      | 1,524,586        | 

Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality.

**Infrastructure:**
We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.

**Ethical Considerations and Limitations:** 
The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-30m-English is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size).


<!-- ## Citation
```
@misc{granite-embedding-models,
  author = {author 1, author2, ...},
  title = {},
  journal = {},
  volume = {},
  year = {2024},
  url = {https://arxiv.org/abs/0000.00000},
}
``` -->