metadata
model-index:
- name: FRIDA
results:
- dataset:
config: default
name: MTEB CEDRClassification (default)
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
split: test
type: ai-forever/cedr-classification
metrics:
- type: accuracy
value: 64.60148777895856
- type: f1
value: 70.36630348039266
- type: lrap
value: 92.47290116896953
- type: main_score
value: 64.60148777895856
task:
type: MultilabelClassification
- dataset:
config: default
name: MTEB GeoreviewClassification (default)
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
split: test
type: ai-forever/georeview-classification
metrics:
- type: accuracy
value: 57.70996093750001
- type: f1
value: 53.18542982057098
- type: f1_weighted
value: 53.17663229582108
- type: main_score
value: 57.70996093750001
task:
type: Classification
- dataset:
config: default
name: MTEB GeoreviewClusteringP2P (default)
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
split: test
type: ai-forever/georeview-clustering-p2p
metrics:
- type: main_score
value: 78.25468393043356
- type: v_measure
value: 78.25468393043356
- type: v_measure_std
value: 0.5094366871364238
task:
type: Clustering
- dataset:
config: default
name: MTEB HeadlineClassification (default)
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
split: test
type: ai-forever/headline-classification
metrics:
- type: accuracy
value: 89.0185546875
- type: f1
value: 88.993933120612
- type: f1_weighted
value: 88.99276764225768
- type: main_score
value: 89.0185546875
task:
type: Classification
- dataset:
config: default
name: MTEB InappropriatenessClassification (default)
revision: 601651fdc45ef243751676e62dd7a19f491c0285
split: test
type: ai-forever/inappropriateness-classification
metrics:
- type: accuracy
value: 78.330078125
- type: ap
value: 73.17856750532495
- type: ap_weighted
value: 73.17856750532495
- type: f1
value: 78.20169867599041
- type: f1_weighted
value: 78.20169867599041
- type: main_score
value: 78.330078125
task:
type: Classification
- dataset:
config: default
name: MTEB KinopoiskClassification (default)
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
split: test
type: ai-forever/kinopoisk-sentiment-classification
metrics:
- type: accuracy
value: 70.46666666666665
- type: f1
value: 65.83951766538878
- type: f1_weighted
value: 65.83951766538878
- type: main_score
value: 70.46666666666665
task:
type: Classification
- dataset:
config: ru
name: MTEB MIRACLReranking (ru)
revision: 6d1962c527217f8927fca80f890f14f36b2802af
split: dev
type: miracl/mmteb-miracl-reranking
metrics:
- type: MAP@1(MIRACL)
value: 39.023
- type: MAP@10(MIRACL)
value: 60.208
- type: MAP@100(MIRACL)
value: 61.672000000000004
- type: MAP@1000(MIRACL)
value: 61.672000000000004
- type: MAP@20(MIRACL)
value: 61.30799999999999
- type: MAP@3(MIRACL)
value: 53.33
- type: MAP@5(MIRACL)
value: 57.289
- type: NDCG@1(MIRACL)
value: 63.352
- type: NDCG@10(MIRACL)
value: 66.042
- type: NDCG@100(MIRACL)
value: 68.702
- type: NDCG@1000(MIRACL)
value: 68.702
- type: NDCG@20(MIRACL)
value: 67.768
- type: NDCG@3(MIRACL)
value: 61.925
- type: NDCG@5(MIRACL)
value: 63.327
- type: P@1(MIRACL)
value: 63.352
- type: P@10(MIRACL)
value: 16.512
- type: P@100(MIRACL)
value: 1.9529999999999998
- type: P@1000(MIRACL)
value: 0.19499999999999998
- type: P@20(MIRACL)
value: 9.13
- type: P@3(MIRACL)
value: 37.878
- type: P@5(MIRACL)
value: 27.586
- type: Recall@1(MIRACL)
value: 39.023
- type: Recall@10(MIRACL)
value: 72.35000000000001
- type: Recall@100(MIRACL)
value: 79.952
- type: Recall@1000(MIRACL)
value: 79.952
- type: Recall@20(MIRACL)
value: 76.828
- type: Recall@3(MIRACL)
value: 57.769999999999996
- type: Recall@5(MIRACL)
value: 64.91900000000001
- type: main_score
value: 66.042
- type: nAUC_MAP@1000_diff1(MIRACL)
value: 27.150388833033052
- type: nAUC_MAP@1000_max(MIRACL)
value: 55.15672274267081
- type: nAUC_MAP@1000_std(MIRACL)
value: 30.088939934575553
- type: nAUC_MAP@100_diff1(MIRACL)
value: 27.150388833033052
- type: nAUC_MAP@100_max(MIRACL)
value: 55.15672274267081
- type: nAUC_MAP@100_std(MIRACL)
value: 30.088939934575553
- type: nAUC_MAP@10_diff1(MIRACL)
value: 27.853691773641742
- type: nAUC_MAP@10_max(MIRACL)
value: 52.89390350055654
- type: nAUC_MAP@10_std(MIRACL)
value: 28.08732516551691
- type: nAUC_MAP@1_diff1(MIRACL)
value: 43.23179150244192
- type: nAUC_MAP@1_max(MIRACL)
value: 29.923943954188864
- type: nAUC_MAP@1_std(MIRACL)
value: 7.447084370195121
- type: nAUC_MAP@20_diff1(MIRACL)
value: 27.328384072311675
- type: nAUC_MAP@20_max(MIRACL)
value: 54.60286379835721
- type: nAUC_MAP@20_std(MIRACL)
value: 29.8084128980043
- type: nAUC_MAP@3_diff1(MIRACL)
value: 31.244971536944554
- type: nAUC_MAP@3_max(MIRACL)
value: 43.63984692803854
- type: nAUC_MAP@3_std(MIRACL)
value: 18.609234683765887
- type: nAUC_MAP@5_diff1(MIRACL)
value: 29.088760492638286
- type: nAUC_MAP@5_max(MIRACL)
value: 48.30474364461509
- type: nAUC_MAP@5_std(MIRACL)
value: 23.817514353844224
- type: nAUC_NDCG@1000_diff1(MIRACL)
value: 23.12754356408408
- type: nAUC_NDCG@1000_max(MIRACL)
value: 64.24894553363303
- type: nAUC_NDCG@1000_std(MIRACL)
value: 38.19318050598967
- type: nAUC_NDCG@100_diff1(MIRACL)
value: 23.12754356408408
- type: nAUC_NDCG@100_max(MIRACL)
value: 64.24894553363303
- type: nAUC_NDCG@100_std(MIRACL)
value: 38.19318050598967
- type: nAUC_NDCG@10_diff1(MIRACL)
value: 24.779856373697275
- type: nAUC_NDCG@10_max(MIRACL)
value: 60.4054459738118
- type: nAUC_NDCG@10_std(MIRACL)
value: 35.148950441182784
- type: nAUC_NDCG@1_diff1(MIRACL)
value: 35.605865569438556
- type: nAUC_NDCG@1_max(MIRACL)
value: 65.77787399715454
- type: nAUC_NDCG@1_std(MIRACL)
value: 34.34726892885082
- type: nAUC_NDCG@20_diff1(MIRACL)
value: 23.71231783125691
- type: nAUC_NDCG@20_max(MIRACL)
value: 62.89676599488004
- type: nAUC_NDCG@20_std(MIRACL)
value: 37.697052941884316
- type: nAUC_NDCG@3_diff1(MIRACL)
value: 26.109027741640865
- type: nAUC_NDCG@3_max(MIRACL)
value: 56.22356793638693
- type: nAUC_NDCG@3_std(MIRACL)
value: 29.9437568508688
- type: nAUC_NDCG@5_diff1(MIRACL)
value: 25.98644715327336
- type: nAUC_NDCG@5_max(MIRACL)
value: 56.25032008404774
- type: nAUC_NDCG@5_std(MIRACL)
value: 31.581899860862578
- type: nAUC_P@1000_diff1(MIRACL)
value: -18.29912787064644
- type: nAUC_P@1000_max(MIRACL)
value: 31.811344878776087
- type: nAUC_P@1000_std(MIRACL)
value: 30.163820183304914
- type: nAUC_P@100_diff1(MIRACL)
value: -18.299127870646405
- type: nAUC_P@100_max(MIRACL)
value: 31.811344878776133
- type: nAUC_P@100_std(MIRACL)
value: 30.163820183304956
- type: nAUC_P@10_diff1(MIRACL)
value: -15.96416268531149
- type: nAUC_P@10_max(MIRACL)
value: 36.989578896466526
- type: nAUC_P@10_std(MIRACL)
value: 34.54507111688143
- type: nAUC_P@1_diff1(MIRACL)
value: 35.605865569438556
- type: nAUC_P@1_max(MIRACL)
value: 65.77787399715454
- type: nAUC_P@1_std(MIRACL)
value: 34.34726892885082
- type: nAUC_P@20_diff1(MIRACL)
value: -17.443963421383287
- type: nAUC_P@20_max(MIRACL)
value: 34.309618168778385
- type: nAUC_P@20_std(MIRACL)
value: 33.38820956485373
- type: nAUC_P@3_diff1(MIRACL)
value: -8.533621861815652
- type: nAUC_P@3_max(MIRACL)
value: 45.90408386776497
- type: nAUC_P@3_std(MIRACL)
value: 34.50459351305535
- type: nAUC_P@5_diff1(MIRACL)
value: -13.207968899314865
- type: nAUC_P@5_max(MIRACL)
value: 40.37718282248973
- type: nAUC_P@5_std(MIRACL)
value: 35.601417332196206
- type: nAUC_Recall@1000_diff1(MIRACL)
value: 7.907304198177226
- type: nAUC_Recall@1000_max(MIRACL)
value: 77.82197832361145
- type: nAUC_Recall@1000_std(MIRACL)
value: 52.66957487246724
- type: nAUC_Recall@100_diff1(MIRACL)
value: 7.907304198177226
- type: nAUC_Recall@100_max(MIRACL)
value: 77.82197832361145
- type: nAUC_Recall@100_std(MIRACL)
value: 52.66957487246724
- type: nAUC_Recall@10_diff1(MIRACL)
value: 15.498121023488693
- type: nAUC_Recall@10_max(MIRACL)
value: 62.24320529338724
- type: nAUC_Recall@10_std(MIRACL)
value: 40.60221460946224
- type: nAUC_Recall@1_diff1(MIRACL)
value: 43.23179150244192
- type: nAUC_Recall@1_max(MIRACL)
value: 29.923943954188864
- type: nAUC_Recall@1_std(MIRACL)
value: 7.447084370195121
- type: nAUC_Recall@20_diff1(MIRACL)
value: 11.457044176116248
- type: nAUC_Recall@20_max(MIRACL)
value: 70.3493054342368
- type: nAUC_Recall@20_std(MIRACL)
value: 49.27124296325928
- type: nAUC_Recall@3_diff1(MIRACL)
value: 25.12077828977941
- type: nAUC_Recall@3_max(MIRACL)
value: 42.903379317937166
- type: nAUC_Recall@3_std(MIRACL)
value: 20.324501722161497
- type: nAUC_Recall@5_diff1(MIRACL)
value: 20.925701235197977
- type: nAUC_Recall@5_max(MIRACL)
value: 49.85323960390812
- type: nAUC_Recall@5_std(MIRACL)
value: 29.04484539530469
task:
type: Reranking
- dataset:
config: ru
name: MTEB MIRACLRetrieval (ru)
revision: main
split: dev
type: miracl/mmteb-miracl
metrics:
- type: main_score
value: 71.882
- type: map_at_1
value: 37.913000000000004
- type: map_at_10
value: 62.604000000000006
- type: map_at_100
value: 64.925
- type: map_at_1000
value: 64.992
- type: map_at_20
value: 64.081
- type: map_at_3
value: 55.212
- type: map_at_5
value: 59.445
- type: mrr_at_1
value: 73.24281150159744
- type: mrr_at_10
value: 81.65043866321825
- type: mrr_at_100
value: 81.85391378818977
- type: mrr_at_1000
value: 81.85753390802569
- type: mrr_at_20
value: 81.81045606130179
- type: mrr_at_3
value: 80.56443024494146
- type: mrr_at_5
value: 81.30724174653893
- type: nauc_map_at_1000_diff1
value: 26.962150235593356
- type: nauc_map_at_1000_max
value: 29.234958037854568
- type: nauc_map_at_1000_std
value: -2.4294465103633884
- type: nauc_map_at_100_diff1
value: 26.92990252114163
- type: nauc_map_at_100_max
value: 29.206328533120118
- type: nauc_map_at_100_std
value: -2.437371090941197
- type: nauc_map_at_10_diff1
value: 25.758265691179226
- type: nauc_map_at_10_max
value: 26.949978490795317
- type: nauc_map_at_10_std
value: -5.484961002106038
- type: nauc_map_at_1_diff1
value: 34.70849461278043
- type: nauc_map_at_1_max
value: 12.778570893623042
- type: nauc_map_at_1_std
value: -13.018292652743938
- type: nauc_map_at_20_diff1
value: 26.659923008218268
- type: nauc_map_at_20_max
value: 28.341440871568185
- type: nauc_map_at_20_std
value: -3.614549844913084
- type: nauc_map_at_3_diff1
value: 27.197629021438203
- type: nauc_map_at_3_max
value: 20.701094874050856
- type: nauc_map_at_3_std
value: -12.062992301112041
- type: nauc_map_at_5_diff1
value: 25.51793537203295
- type: nauc_map_at_5_max
value: 23.80396771243794
- type: nauc_map_at_5_std
value: -8.920465695323575
- type: nauc_mrr_at_1000_diff1
value: 45.14819989592967
- type: nauc_mrr_at_1000_max
value: 53.29202156141053
- type: nauc_mrr_at_1000_std
value: 18.037336462510524
- type: nauc_mrr_at_100_diff1
value: 45.15287600228451
- type: nauc_mrr_at_100_max
value: 53.29979751928615
- type: nauc_mrr_at_100_std
value: 18.04996604778386
- type: nauc_mrr_at_10_diff1
value: 44.96865105944474
- type: nauc_mrr_at_10_max
value: 53.53323465323092
- type: nauc_mrr_at_10_std
value: 18.25001344917689
- type: nauc_mrr_at_1_diff1
value: 46.16604946873163
- type: nauc_mrr_at_1_max
value: 48.573651103547874
- type: nauc_mrr_at_1_std
value: 13.764871626330915
- type: nauc_mrr_at_20_diff1
value: 45.11925458479102
- type: nauc_mrr_at_20_max
value: 53.35685123898342
- type: nauc_mrr_at_20_std
value: 18.127344968819905
- type: nauc_mrr_at_3_diff1
value: 45.377195452730234
- type: nauc_mrr_at_3_max
value: 53.35146309217089
- type: nauc_mrr_at_3_std
value: 17.47105877186237
- type: nauc_mrr_at_5_diff1
value: 45.00525578771549
- type: nauc_mrr_at_5_max
value: 53.76227254707128
- type: nauc_mrr_at_5_std
value: 18.437290060746957
- type: nauc_ndcg_at_1000_diff1
value: 31.19215594457491
- type: nauc_ndcg_at_1000_max
value: 38.09555406458668
- type: nauc_ndcg_at_1000_std
value: 7.225628621238009
- type: nauc_ndcg_at_100_diff1
value: 30.726331247999934
- type: nauc_ndcg_at_100_max
value: 37.81369589418277
- type: nauc_ndcg_at_100_std
value: 7.242855238555071
- type: nauc_ndcg_at_10_diff1
value: 27.514048333744835
- type: nauc_ndcg_at_10_max
value: 33.10990399385253
- type: nauc_ndcg_at_10_std
value: 0.3051899572112002
- type: nauc_ndcg_at_1_diff1
value: 47.06089085235751
- type: nauc_ndcg_at_1_max
value: 47.7300872370495
- type: nauc_ndcg_at_1_std
value: 12.468605493613916
- type: nauc_ndcg_at_20_diff1
value: 29.404215438764496
- type: nauc_ndcg_at_20_max
value: 35.26967886796471
- type: nauc_ndcg_at_20_std
value: 3.7214697890813353
- type: nauc_ndcg_at_3_diff1
value: 29.448848639643067
- type: nauc_ndcg_at_3_max
value: 33.85912412370657
- type: nauc_ndcg_at_3_std
value: 0.895453646819452
- type: nauc_ndcg_at_5_diff1
value: 26.916649012613526
- type: nauc_ndcg_at_5_max
value: 30.899005979291644
- type: nauc_ndcg_at_5_std
value: -1.0001575639156615
- type: nauc_precision_at_1000_diff1
value: -8.492004667432635
- type: nauc_precision_at_1000_max
value: 14.970190384017679
- type: nauc_precision_at_1000_std
value: 32.871386621137816
- type: nauc_precision_at_100_diff1
value: -8.287314133999967
- type: nauc_precision_at_100_max
value: 17.794821961284736
- type: nauc_precision_at_100_std
value: 35.092483550562
- type: nauc_precision_at_10_diff1
value: -7.594128993028063
- type: nauc_precision_at_10_max
value: 24.691446370325732
- type: nauc_precision_at_10_std
value: 30.126552282608493
- type: nauc_precision_at_1_diff1
value: 47.06089085235751
- type: nauc_precision_at_1_max
value: 47.7300872370495
- type: nauc_precision_at_1_std
value: 12.468605493613916
- type: nauc_precision_at_20_diff1
value: -6.503872195775146
- type: nauc_precision_at_20_max
value: 21.789730053141312
- type: nauc_precision_at_20_std
value: 32.61349377558794
- type: nauc_precision_at_3_diff1
value: 0.67417079971061
- type: nauc_precision_at_3_max
value: 30.793871354370662
- type: nauc_precision_at_3_std
value: 18.35266479252011
- type: nauc_precision_at_5_diff1
value: -7.088881730215777
- type: nauc_precision_at_5_max
value: 26.539771712769006
- type: nauc_precision_at_5_std
value: 24.116262291865834
- type: nauc_recall_at_1000_diff1
value: 34.53263588412461
- type: nauc_recall_at_1000_max
value: 63.54157869100173
- type: nauc_recall_at_1000_std
value: 64.19854844792808
- type: nauc_recall_at_100_diff1
value: 22.86564728642275
- type: nauc_recall_at_100_max
value: 40.350507162549825
- type: nauc_recall_at_100_std
value: 29.24492545863015
- type: nauc_recall_at_10_diff1
value: 15.384818367225009
- type: nauc_recall_at_10_max
value: 24.41108571453699
- type: nauc_recall_at_10_std
value: -3.9216160585776323
- type: nauc_recall_at_1_diff1
value: 34.70849461278043
- type: nauc_recall_at_1_max
value: 12.778570893623042
- type: nauc_recall_at_1_std
value: -13.018292652743938
- type: nauc_recall_at_20_diff1
value: 18.122499000084208
- type: nauc_recall_at_20_max
value: 26.63104220179424
- type: nauc_recall_at_20_std
value: 3.969217732521512
- type: nauc_recall_at_3_diff1
value: 21.413050725250116
- type: nauc_recall_at_3_max
value: 16.18894988386887
- type: nauc_recall_at_3_std
value: -15.24884339282375
- type: nauc_recall_at_5_diff1
value: 16.35673072212927
- type: nauc_recall_at_5_max
value: 18.607003829267846
- type: nauc_recall_at_5_std
value: -10.463525876945454
- type: ndcg_at_1
value: 72.923
- type: ndcg_at_10
value: 71.882
- type: ndcg_at_100
value: 77.09899999999999
- type: ndcg_at_1000
value: 77.835
- type: ndcg_at_20
value: 74.497
- type: ndcg_at_3
value: 68.504
- type: ndcg_at_5
value: 69.068
- type: precision_at_1
value: 72.923
- type: precision_at_10
value: 19.936
- type: precision_at_100
value: 2.6310000000000002
- type: precision_at_1000
value: 0.27799999999999997
- type: precision_at_20
value: 11.33
- type: precision_at_3
value: 45.927
- type: precision_at_5
value: 33.131
- type: recall_at_1
value: 37.913000000000004
- type: recall_at_10
value: 78.365
- type: recall_at_100
value: 94.348
- type: recall_at_1000
value: 98.187
- type: recall_at_20
value: 85.229
- type: recall_at_3
value: 61.42999999999999
- type: recall_at_5
value: 69.56700000000001
task:
type: Retrieval
- dataset:
config: ru
name: MTEB MassiveIntentClassification (ru)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 79.11903160726294
- type: f1
value: 76.22609082694545
- type: f1_weighted
value: 77.81461248063566
- type: main_score
value: 79.11903160726294
task:
type: Classification
- dataset:
config: ru
name: MTEB MassiveScenarioClassification (ru)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 88.80632145258912
- type: f1
value: 87.53157475314829
- type: f1_weighted
value: 88.22733432521495
- type: main_score
value: 88.80632145258912
task:
type: Classification
- dataset:
config: default
name: MTEB RUParaPhraserSTS (default)
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
split: test
type: merionum/ru_paraphraser
metrics:
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value: 72.70307124858925
- type: cosine_spearman
value: 78.09439086920204
- type: euclidean_pearson
value: 76.2033672014715
- type: euclidean_spearman
value: 78.09439086920204
- type: main_score
value: 78.09439086920204
- type: manhattan_pearson
value: 76.11750470223116
- type: manhattan_spearman
value: 78.01081063503413
- type: pearson
value: 72.70307124858925
- type: spearman
value: 78.09439086920204
task:
type: STS
- dataset:
config: default
name: MTEB RiaNewsRetrieval (default)
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
split: test
type: ai-forever/ria-news-retrieval
metrics:
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value: 78.79
- type: map_at_10
value: 84.516
- type: map_at_100
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- type: map_at_1000
value: 84.685
- type: map_at_20
value: 84.624
- type: map_at_3
value: 83.722
- type: map_at_5
value: 84.246
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value: 78.78
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value: 84.68390840473289
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- type: precision_at_1
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- type: recall_at_1
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- type: recall_at_10
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- type: recall_at_1000
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- type: recall_at_5
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task:
type: Retrieval
- dataset:
config: default
name: MTEB RuBQReranking (default)
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
split: test
type: ai-forever/rubq-reranking
metrics:
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value: 77.07394404835635
- type: map
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- type: mrr
value: 82.53144412718882
- type: nAUC_map_diff1
value: 45.29805217456628
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value: 54.783994737367046
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value: 45.68526733900048
- type: nAUC_mrr_std
value: 28.22466385500339
task:
type: Reranking
- dataset:
config: default
name: MTEB RuBQRetrieval (default)
revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
split: test
type: ai-forever/rubq-retrieval
metrics:
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- type: precision_at_10
value: 13.327
- type: precision_at_100
value: 1.5559999999999998
- type: precision_at_1000
value: 0.164
- type: precision_at_20
value: 7.119000000000001
- type: precision_at_3
value: 35.599
- type: precision_at_5
value: 23.936
- type: recall_at_1
value: 47.370000000000005
- type: recall_at_10
value: 82.16
- type: recall_at_100
value: 93.34
- type: recall_at_1000
value: 98.202
- type: recall_at_20
value: 86.687
- type: recall_at_3
value: 69.319
- type: recall_at_5
value: 75.637
task:
type: Retrieval
- dataset:
config: default
name: MTEB RuReviewsClassification (default)
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
split: test
type: ai-forever/ru-reviews-classification
metrics:
- type: accuracy
value: 75.0537109375
- type: f1
value: 74.00523205209554
- type: f1_weighted
value: 74.00436782840376
- type: main_score
value: 75.0537109375
task:
type: Classification
- dataset:
config: default
name: MTEB RuSTSBenchmarkSTS (default)
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
split: test
type: ai-forever/ru-stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 81.10255413476487
- type: cosine_spearman
value: 81.40020843157141
- type: euclidean_pearson
value: 81.25155479902466
- type: euclidean_spearman
value: 81.40020831064922
- type: main_score
value: 81.40020843157141
- type: manhattan_pearson
value: 81.1493715249014
- type: manhattan_spearman
value: 81.30973667941649
- type: pearson
value: 81.10255413476487
- type: spearman
value: 81.40020843157141
task:
type: STS
- dataset:
config: default
name: MTEB RuSciBenchGRNTIClassification (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
- type: accuracy
value: 69.8974609375
- type: f1
value: 68.57837564785511
- type: f1_weighted
value: 68.59030489460784
- type: main_score
value: 69.8974609375
task:
type: Classification
- dataset:
config: default
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
split: test
type: ai-forever/ru-scibench-grnti-classification
metrics:
- type: main_score
value: 67.03880348548029
- type: v_measure
value: 67.03880348548029
- type: v_measure_std
value: 0.6126278133139618
task:
type: Clustering
- dataset:
config: default
name: MTEB RuSciBenchOECDClassification (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
- type: accuracy
value: 54.63378906250001
- type: f1
value: 51.34306420274629
- type: f1_weighted
value: 51.33495867493914
- type: main_score
value: 54.63378906250001
task:
type: Classification
- dataset:
config: default
name: MTEB RuSciBenchOECDClusteringP2P (default)
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
split: test
type: ai-forever/ru-scibench-oecd-classification
metrics:
- type: main_score
value: 56.55947121159027
- type: v_measure
value: 56.55947121159027
- type: v_measure_std
value: 0.5498882006880662
task:
type: Clustering
- dataset:
config: ru
name: MTEB STS22 (ru)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 61.833294921667914
- type: cosine_spearman
value: 63.53967536726357
- type: euclidean_pearson
value: 60.382865218855805
- type: euclidean_spearman
value: 63.53967536726357
- type: main_score
value: 63.53967536726357
- type: manhattan_pearson
value: 60.24879015304578
- type: manhattan_spearman
value: 63.42305760430092
- type: pearson
value: 61.833294921667914
- type: spearman
value: 63.53967536726357
task:
type: STS
- dataset:
config: default
name: MTEB SensitiveTopicsClassification (default)
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
split: test
type: ai-forever/sensitive-topics-classification
metrics:
- type: accuracy
value: 39.8193359375
- type: f1
value: 55.46591740935434
- type: lrap
value: 66.50980631510454
- type: main_score
value: 39.8193359375
task:
type: MultilabelClassification
- dataset:
config: default
name: MTEB TERRa (default)
revision: 7b58f24536063837d644aab9a023c62199b2a612
split: dev
type: ai-forever/terra-pairclassification
metrics:
- type: cosine_accuracy
value: 66.77524429967427
- type: cosine_accuracy_threshold
value: 55.58975338935852
- type: cosine_ap
value: 66.4567219323658
- type: cosine_f1
value: 70.64676616915423
- type: cosine_f1_threshold
value: 45.55969536304474
- type: cosine_precision
value: 57.028112449799195
- type: cosine_recall
value: 92.81045751633987
- type: dot_accuracy
value: 66.77524429967427
- type: dot_accuracy_threshold
value: 55.589759349823
- type: dot_ap
value: 66.4567219323658
- type: dot_f1
value: 70.64676616915423
- type: dot_f1_threshold
value: 45.55969536304474
- type: dot_precision
value: 57.028112449799195
- type: dot_recall
value: 92.81045751633987
- type: euclidean_accuracy
value: 66.77524429967427
- type: euclidean_accuracy_threshold
value: 94.24455165863037
- type: euclidean_ap
value: 66.4567219323658
- type: euclidean_f1
value: 70.64676616915423
- type: euclidean_f1_threshold
value: 104.34587001800537
- type: euclidean_precision
value: 57.028112449799195
- type: euclidean_recall
value: 92.81045751633987
- type: main_score
value: 66.4567219323658
- type: manhattan_accuracy
value: 66.77524429967427
- type: manhattan_accuracy_threshold
value: 2865.5345916748047
- type: manhattan_ap
value: 66.26659863769075
- type: manhattan_f1
value: 70.8542713567839
- type: manhattan_f1_threshold
value: 3212.3912811279297
- type: manhattan_precision
value: 57.55102040816327
- type: manhattan_recall
value: 92.15686274509804
- type: max_accuracy
value: 66.77524429967427
- type: max_ap
value: 66.4567219323658
- type: max_f1
value: 70.8542713567839
- type: max_precision
value: 57.55102040816327
- type: max_recall
value: 92.81045751633987
- type: similarity_accuracy
value: 66.77524429967427
- type: similarity_accuracy_threshold
value: 55.58975338935852
- type: similarity_ap
value: 66.4567219323658
- type: similarity_f1
value: 70.64676616915423
- type: similarity_f1_threshold
value: 45.55969536304474
- type: similarity_precision
value: 57.028112449799195
- type: similarity_recall
value: 92.81045751633987
task:
type: PairClassification
license: mit
language:
- ru
- en
tags:
- mteb
- transformers
- sentence-transformers
base_model: ai-forever/FRED-T5-1.7B
pipeline_tag: feature-extraction
Model Card for FRIDA
FRIDA is a full-scale finetuned general text embedding model inspired by denoising architecture based on T5. The model is based on the encoder part of FRED-T5 model and continues research of text embedding models (ruMTEB, ru-en-RoSBERTa). It has been pre-trained on a Russian-English dataset and fine-tuned for improved performance on the target task.
For more model details please refer to our technical report [TODO].
Usage
The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
We use the following basic rules to choose a prefix:
"search_query: "and"search_document: "prefixes are for answer or relevant paragraph retrieval"paraphrase: "prefix is for symmetric paraphrasing related tasks (STS, paraphrase mining, deduplication)"categorize: "prefix is for asymmetric matching of document title and body (e.g. news, scientific papers, social posts)"categorize_sentiment: "prefix is for any tasks that rely on sentiment features (e.g. hate, toxic, emotion)"categorize_topic: "prefix is intended for tasks where you need to group texts by topic"categorize_entailment: "prefix is for textual entailment task (NLI)
To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets.
Below are examples of texts encoding using the Transformers and SentenceTransformers libraries.
Transformers
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, T5EncoderModel
def pool(hidden_state, mask, pooling_method="cls"):
if pooling_method == "mean":
s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)
d = mask.sum(axis=1, keepdim=True).float()
return s / d
elif pooling_method == "cls":
return hidden_state[:, 0]
inputs = [
#
"paraphrase: В Ярославской области разрешили работу бань, но без посетителей",
"categorize_entailment: Женщину доставили в больницу, за ее жизнь сейчас борются врачи.",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
#
"paraphrase: Ярославским баням разрешили работать без посетителей",
"categorize_entailment: Женщину спасают врачи.",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование."
]
tokenizer = AutoTokenizer.from_pretrained("ai-forever/FRIDA")
model = T5EncoderModel.from_pretrained("ai-forever/FRIDA")
tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
outputs = model(**tokenized_inputs)
embeddings = pool(
outputs.last_hidden_state,
tokenized_inputs["attention_mask"],
pooling_method="cls" # or try "mean"
)
embeddings = F.normalize(embeddings, p=2, dim=1)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.9360030293464661, 0.8591322302818298, 0.728583037853241]
SentenceTransformers
from sentence_transformers import SentenceTransformer
inputs = [
#
"paraphrase: В Ярославской области разрешили работу бань, но без посетителей",
"categorize_entailment: Женщину доставили в больницу, за ее жизнь сейчас борются врачи.",
"search_query: Сколько программистов нужно, чтобы вкрутить лампочку?",
#
"paraphrase: Ярославским баням разрешили работать без посетителей",
"categorize_entailment: Женщину спасают врачи.",
"search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование."
]
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/FRIDA")
# embeddings are normalized by default
embeddings = model.encode(inputs, convert_to_tensor=True)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diag().tolist())
# [0.9360026717185974, 0.8591331243515015, 0.7285830974578857]
or using prompts (sentence-transformers>=2.4.0):
from sentence_transformers import SentenceTransformer
# loads model with CLS pooling
model = SentenceTransformer("ai-forever/FRIDA")
paraphrase = model.encode(["В Ярославской области разрешили работу бань, но без посетителей", "Ярославским баням разрешили работать без посетителей"], prompt_name="paraphrase")
print(paraphrase[0] @ paraphrase[1].T) # 0.9360032
categorize_entailment = model.encode(["Женщину доставили в больницу, за ее жизнь сейчас борются врачи.", "Женщину спасают врачи."], prompt_name="categorize_entailment")
print(categorize_entailment[0] @ categorize_entailment[1].T) # 0.8591322
query_embedding = model.encode("Сколько программистов нужно, чтобы вкрутить лампочку?", prompt_name="search_query")
document_embedding = model.encode("Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", prompt_name="search_document")
print(query_embedding @ document_embedding.T) # 0.7285831
Authors
- SaluteDevices AI for B2C RnD Team.
- Artem Snegirev: HF profile, Github;
- Anna Maksimova HF profile;
- Aleksandr Abramov: HF profile, Github, Kaggle Competitions Master
Citation
@misc{TODO
}
Limitations
The model is designed to process texts in Russian, the quality in English is unknown. Maximum input text length is limited to 512 tokens.