Upload FRIDA model copy

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+img.jpg filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1536,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -1,3 +1,1697 @@
 ---
+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
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+    - type: nauc_map_at_1000_max
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+    - type: nauc_map_at_100_diff1
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+    - type: nauc_mrr_at_100_std
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+    - type: nauc_mrr_at_10_diff1
+      value: 44.96865105944474
+    - type: nauc_mrr_at_10_max
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+    - type: nauc_mrr_at_10_std
+      value: 18.25001344917689
+    - type: nauc_mrr_at_1_diff1
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+    - type: nauc_mrr_at_1_std
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+    - type: nauc_precision_at_1000_std
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+    - type: nauc_precision_at_100_diff1
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+    - type: nauc_precision_at_100_max
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+    - type: nauc_precision_at_100_std
+      value: 35.092483550562
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+    - type: nauc_precision_at_10_max
+      value: 24.691446370325732
+    - type: nauc_precision_at_10_std
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+      value: 2.3956102974352356
+    - type: nauc_mrr_at_100_diff1
+      value: 58.9865943101085
+    - type: nauc_mrr_at_100_max
+      value: 49.6248111507265
+    - type: nauc_mrr_at_100_std
+      value: 2.411155095066369
+    - type: nauc_mrr_at_10_diff1
+      value: 58.81758131092919
+    - type: nauc_mrr_at_10_max
+      value: 49.780365572616695
+    - type: nauc_mrr_at_10_std
+      value: 2.7068696565195944
+    - type: nauc_mrr_at_1_diff1
+      value: 61.67036882487055
+    - type: nauc_mrr_at_1_max
+      value: 45.455271042821714
+    - type: nauc_mrr_at_1_std
+      value: -0.9370526815458349
+    - type: nauc_mrr_at_20_diff1
+      value: 58.93674818203478
+    - type: nauc_mrr_at_20_max
+      value: 49.703218108625215
+    - type: nauc_mrr_at_20_std
+      value: 2.4473106598190415
+    - type: nauc_mrr_at_3_diff1
+      value: 59.046856598788445
+    - type: nauc_mrr_at_3_max
+      value: 49.37161726123392
+    - type: nauc_mrr_at_3_std
+      value: 1.5110936686701506
+    - type: nauc_mrr_at_5_diff1
+      value: 58.92289378915668
+    - type: nauc_mrr_at_5_max
+      value: 49.847638994134144
+    - type: nauc_mrr_at_5_std
+      value: 2.420421880131702
+    - type: nauc_ndcg_at_1000_diff1
+      value: 45.56062215161734
+    - type: nauc_ndcg_at_1000_max
+      value: 41.507152286702
+    - type: nauc_ndcg_at_1000_std
+      value: 2.79388283208751
+    - type: nauc_ndcg_at_100_diff1
+      value: 44.84064192570408
+    - type: nauc_ndcg_at_100_max
+      value: 41.50353573562353
+    - type: nauc_ndcg_at_100_std
+      value: 3.1804999773629357
+    - type: nauc_ndcg_at_10_diff1
+      value: 43.341482144213614
+    - type: nauc_ndcg_at_10_max
+      value: 41.159590898395074
+    - type: nauc_ndcg_at_10_std
+      value: 2.945242338240843
+    - type: nauc_ndcg_at_1_diff1
+      value: 62.23623985611396
+    - type: nauc_ndcg_at_1_max
+      value: 45.04945770947091
+    - type: nauc_ndcg_at_1_std
+      value: -0.8804967656575725
+    - type: nauc_ndcg_at_20_diff1
+      value: 43.905372612093664
+    - type: nauc_ndcg_at_20_max
+      value: 41.797709837872446
+    - type: nauc_ndcg_at_20_std
+      value: 3.1853356915569653
+    - type: nauc_ndcg_at_3_diff1
+      value: 44.18163998834299
+    - type: nauc_ndcg_at_3_max
+      value: 38.352891017864636
+    - type: nauc_ndcg_at_3_std
+      value: -0.8235767021150929
+    - type: nauc_ndcg_at_5_diff1
+      value: 43.41374688421302
+    - type: nauc_ndcg_at_5_max
+      value: 40.390365601593956
+    - type: nauc_ndcg_at_5_std
+      value: 1.6743650108127537
+    - type: nauc_precision_at_1000_diff1
+      value: -9.711058370691381
+    - type: nauc_precision_at_1000_max
+      value: 6.97321343449286
+    - type: nauc_precision_at_1000_std
+      value: 7.933531916622121
+    - type: nauc_precision_at_100_diff1
+      value: -8.247029644152319
+    - type: nauc_precision_at_100_max
+      value: 10.86740140944616
+    - type: nauc_precision_at_100_std
+      value: 9.581885544675918
+    - type: nauc_precision_at_10_diff1
+      value: -2.409043695429943
+    - type: nauc_precision_at_10_max
+      value: 21.04733206074314
+    - type: nauc_precision_at_10_std
+      value: 10.03334651647101
+    - type: nauc_precision_at_1_diff1
+      value: 62.23623985611396
+    - type: nauc_precision_at_1_max
+      value: 45.04945770947091
+    - type: nauc_precision_at_1_std
+      value: -0.8804967656575725
+    - type: nauc_precision_at_20_diff1
+      value: -5.230303656931621
+    - type: nauc_precision_at_20_max
+      value: 17.77799716919181
+    - type: nauc_precision_at_20_std
+      value: 10.739127998618654
+    - type: nauc_precision_at_3_diff1
+      value: 10.40376424999862
+    - type: nauc_precision_at_3_max
+      value: 30.933333400254035
+    - type: nauc_precision_at_3_std
+      value: 6.126209127968004
+    - type: nauc_precision_at_5_diff1
+      value: 3.147398101830739
+    - type: nauc_precision_at_5_max
+      value: 27.1746309955971
+    - type: nauc_precision_at_5_std
+      value: 8.874723615388788
+    - type: nauc_recall_at_1000_diff1
+      value: 5.055940692380908
+    - type: nauc_recall_at_1000_max
+      value: 22.42031123370267
+    - type: nauc_recall_at_1000_std
+      value: 27.75476692527869
+    - type: nauc_recall_at_100_diff1
+      value: 17.86391178198642
+    - type: nauc_recall_at_100_max
+      value: 34.776134863678955
+    - type: nauc_recall_at_100_std
+      value: 18.96377158778504
+    - type: nauc_recall_at_10_diff1
+      value: 24.863097695413597
+    - type: nauc_recall_at_10_max
+      value: 37.697411651507444
+    - type: nauc_recall_at_10_std
+      value: 9.519849994253967
+    - type: nauc_recall_at_1_diff1
+      value: 49.56605205141156
+    - type: nauc_recall_at_1_max
+      value: 26.251096698710384
+    - type: nauc_recall_at_1_std
+      value: -4.580748485387834
+    - type: nauc_recall_at_20_diff1
+      value: 22.440602811005636
+    - type: nauc_recall_at_20_max
+      value: 39.538861316515
+    - type: nauc_recall_at_20_std
+      value: 11.363269553121468
+    - type: nauc_recall_at_3_diff1
+      value: 32.80302839873736
+    - type: nauc_recall_at_3_max
+      value: 32.53105685012729
+    - type: nauc_recall_at_3_std
+      value: -0.7140166410605693
+    - type: nauc_recall_at_5_diff1
+      value: 29.375386639154865
+    - type: nauc_recall_at_5_max
+      value: 36.91045781164083
+    - type: nauc_recall_at_5_std
+      value: 4.725419050262578
+    - type: ndcg_at_1
+      value: 67.13900000000001
+    - type: ndcg_at_10
+      value: 72.392
+    - type: ndcg_at_100
+      value: 75.25800000000001
+    - type: ndcg_at_1000
+      value: 75.982
+    - type: ndcg_at_20
+      value: 73.783
+    - type: ndcg_at_3
+      value: 67.269
+    - type: ndcg_at_5
+      value: 69.807
+    - type: precision_at_1
+      value: 67.13900000000001
+    - 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
+datasets:
+- ai-forever/solyanka
 ---
+
+# Model Card for FRIDA
+
+<figure>
+  <img src="img.jpg">
+</figure>
+
+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](https://arxiv.org/abs/2309.10931) model and continues research of text embedding models ([ruMTEB](https://arxiv.org/abs/2408.12503), [ru-en-RoSBERTa](https://huggingface.co/ai-forever/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 [article](https://habr.com/ru/companies/sberdevices/articles/909924/) (RU).
+
+## 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
+
+```python
+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
+
+```python
+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):
+
+```python
+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](https://sberdevices.ru/) AI for B2C RnD Team.
++ Artem Snegirev: [HF profile](https://huggingface.co/artemsnegirev), [Github](https://github.com/artemsnegirev);
++ Anna Maksimova [HF profile](https://huggingface.co/anpalmak);
++ Aleksandr Abramov: [HF profile](https://huggingface.co/Andrilko), [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko)
+
+
+## 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.

config.json

@@ -0,0 +1,34 @@
+{
+  "_name_or_path": "FRIDA",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "bos_token_id": 1,
+  "classifier_dropout": 0.0,
+  "d_ff": 4096,
+  "d_kv": 64,
+  "d_model": 1536,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "feed_forward_proj": "gated-gelu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 24,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "use_cache": true,
+  "vocab_size": 93651
+}

config_sentence_transformers.json

@@ -0,0 +1,18 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.7.0",
+    "transformers": "4.40.1",
+    "pytorch": "2.2.1+cu118"
+  },
+  "prompts": {
+    "paraphrase": "paraphrase: ",
+    "search_query": "search_query: ",
+    "search_document": "search_document: ",
+    "categorize": "categorize: ",
+    "categorize_sentiment": "categorize_sentiment: ",
+    "categorize_topic": "categorize_topic: ",
+    "categorize_entailment": "categorize_entailment: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f48a05972031f3e9b4799b548a148215097f148332c8a68c3bf575497d6a0d43
+size 3293631728

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "unk_token": "<unk>"
+}