diff --git "a/README.md" "b/README.md"
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+---
+license: cc-by-nc-4.0
+tags:
+- feature-extraction
+- sentence-similarity
+- mteb
+- sentence-transformers
+language:
+  - multilingual
+  - af
+  - am
+  - ar
+  - as
+  - az
+  - be
+  - bg
+  - bn
+  - br
+  - bs
+  - ca
+  - cs
+  - cy
+  - da
+  - de
+  - el
+  - en
+  - eo
+  - es
+  - et
+  - eu
+  - fa
+  - fi
+  - fr
+  - fy
+  - ga
+  - gd
+  - gl
+  - gu
+  - ha
+  - he
+  - hi
+  - hr
+  - hu
+  - hy
+  - id
+  - is
+  - it
+  - ja
+  - jv
+  - ka
+  - kk
+  - km
+  - kn
+  - ko
+  - ku
+  - ky
+  - la
+  - lo
+  - lt
+  - lv
+  - mg
+  - mk
+  - ml
+  - mn
+  - mr
+  - ms
+  - my
+  - ne
+  - nl
+  - no
+  - om
+  - or
+  - pa
+  - pl
+  - ps
+  - pt
+  - ro
+  - ru
+  - sa
+  - sd
+  - si
+  - sk
+  - sl
+  - so
+  - sq
+  - sr
+  - su
+  - sv
+  - sw
+  - ta
+  - te
+  - th
+  - tl
+  - tr
+  - ug
+  - uk
+  - ur
+  - uz
+  - vi
+  - xh
+  - yi
+  - zh
+inference: false
+library_name: transformers
+model-index:
+- name: jina-embeddings-v3
+  results:
+  - dataset:
+      config: default
+      name: MTEB AFQMC (default)
+      revision: b44c3b011063adb25877c13823db83bb193913c4
+      split: validation
+      type: C-MTEB/AFQMC
+    metrics:
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+      value: 41.74237700998808
+    - type: cosine_spearman
+      value: 43.4726782647566
+    - type: euclidean_pearson
+      value: 42.244585459479964
+    - type: euclidean_spearman
+      value: 43.525070045169606
+    - type: main_score
+      value: 43.4726782647566
+    - type: manhattan_pearson
+      value: 42.04616728224863
+    - type: manhattan_spearman
+      value: 43.308828270754645
+    - type: pearson
+      value: 41.74237700998808
+    - type: spearman
+      value: 43.4726782647566
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB ArguAna-PL (default)
+      revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
+      split: test
+      type: clarin-knext/arguana-pl
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+      name: MTEB MIRACLReranking (ru)
+      revision: 6d1962c527217f8927fca80f890f14f36b2802af
+      split: dev
+      type: miracl/mmteb-miracl-reranking
+    metrics:
+    - type: MAP@1(MIRACL)
+      value: 20.721999999999998
+    - type: MAP@10(MIRACL)
+      value: 33.900999999999996
+    - type: MAP@100(MIRACL)
+      value: 36.813
+    - type: MAP@1000(MIRACL)
+      value: 36.813
+    - type: MAP@20(MIRACL)
+      value: 35.684
+    - type: MAP@3(MIRACL)
+      value: 28.141
+    - type: MAP@5(MIRACL)
+      value: 31.075000000000003
+    - type: NDCG@1(MIRACL)
+      value: 32.799
+    - type: NDCG@10(MIRACL)
+      value: 42.065000000000005
+    - type: NDCG@100(MIRACL)
+      value: 49.730999999999995
+    - type: NDCG@1000(MIRACL)
+      value: 49.730999999999995
+    - type: NDCG@20(MIRACL)
+      value: 46.0
+    - type: NDCG@3(MIRACL)
+      value: 34.481
+    - type: NDCG@5(MIRACL)
+      value: 37.452999999999996
+    - type: P@1(MIRACL)
+      value: 32.799
+    - type: P@10(MIRACL)
+      value: 11.668000000000001
+    - type: P@100(MIRACL)
+      value: 1.9529999999999998
+    - type: P@1000(MIRACL)
+      value: 0.19499999999999998
+    - type: P@20(MIRACL)
+      value: 7.51
+    - type: P@3(MIRACL)
+      value: 20.823
+    - type: P@5(MIRACL)
+      value: 16.728
+    - type: Recall@1(MIRACL)
+      value: 20.721999999999998
+    - type: Recall@10(MIRACL)
+      value: 54.762
+    - type: Recall@100(MIRACL)
+      value: 79.952
+    - type: Recall@1000(MIRACL)
+      value: 79.952
+    - type: Recall@20(MIRACL)
+      value: 66.26100000000001
+    - type: Recall@3(MIRACL)
+      value: 34.410000000000004
+    - type: Recall@5(MIRACL)
+      value: 42.659000000000006
+    - type: main_score
+      value: 42.065000000000005
+    - type: nAUC_MAP@1000_diff1(MIRACL)
+      value: 14.33534992502818
+    - type: nAUC_MAP@1000_max(MIRACL)
+      value: 12.367998764646115
+    - type: nAUC_MAP@1000_std(MIRACL)
+      value: 4.569686002935006
+    - type: nAUC_MAP@100_diff1(MIRACL)
+      value: 14.33534992502818
+    - type: nAUC_MAP@100_max(MIRACL)
+      value: 12.367998764646115
+    - type: nAUC_MAP@100_std(MIRACL)
+      value: 4.569686002935006
+    - type: nAUC_MAP@10_diff1(MIRACL)
+      value: 16.920323975680027
+    - type: nAUC_MAP@10_max(MIRACL)
+      value: 9.327171297204082
+    - type: nAUC_MAP@10_std(MIRACL)
+      value: 3.2039133783079015
+    - type: nAUC_MAP@1_diff1(MIRACL)
+      value: 28.698973487482206
+    - type: nAUC_MAP@1_max(MIRACL)
+      value: 2.9217687660885034
+    - type: nAUC_MAP@1_std(MIRACL)
+      value: -1.1247408800976524
+    - type: nAUC_MAP@20_diff1(MIRACL)
+      value: 15.359083081640476
+    - type: nAUC_MAP@20_max(MIRACL)
+      value: 11.310494233946345
+    - type: nAUC_MAP@20_std(MIRACL)
+      value: 4.4171898386022885
+    - type: nAUC_MAP@3_diff1(MIRACL)
+      value: 22.27430591851617
+    - type: nAUC_MAP@3_max(MIRACL)
+      value: 6.407438291284658
+    - type: nAUC_MAP@3_std(MIRACL)
+      value: 0.9799184530397409
+    - type: nAUC_MAP@5_diff1(MIRACL)
+      value: 19.20571689941054
+    - type: nAUC_MAP@5_max(MIRACL)
+      value: 7.987468654026893
+    - type: nAUC_MAP@5_std(MIRACL)
+      value: 1.8324246565938962
+    - type: nAUC_NDCG@1000_diff1(MIRACL)
+      value: 3.7537669018914768
+    - type: nAUC_NDCG@1000_max(MIRACL)
+      value: 20.7944707840533
+    - type: nAUC_NDCG@1000_std(MIRACL)
+      value: 8.444837055303063
+    - type: nAUC_NDCG@100_diff1(MIRACL)
+      value: 3.7537669018914768
+    - type: nAUC_NDCG@100_max(MIRACL)
+      value: 20.7944707840533
+    - type: nAUC_NDCG@100_std(MIRACL)
+      value: 8.444837055303063
+    - type: nAUC_NDCG@10_diff1(MIRACL)
+      value: 10.829575656103888
+    - type: nAUC_NDCG@10_max(MIRACL)
+      value: 13.0445496498929
+    - type: nAUC_NDCG@10_std(MIRACL)
+      value: 6.050412212625362
+    - type: nAUC_NDCG@1_diff1(MIRACL)
+      value: 19.1388712233292
+    - type: nAUC_NDCG@1_max(MIRACL)
+      value: 10.871900994781642
+    - type: nAUC_NDCG@1_std(MIRACL)
+      value: 3.218568248751811
+    - type: nAUC_NDCG@20_diff1(MIRACL)
+      value: 7.093172181746442
+    - type: nAUC_NDCG@20_max(MIRACL)
+      value: 16.955238078958836
+    - type: nAUC_NDCG@20_std(MIRACL)
+      value: 8.325656379573035
+    - type: nAUC_NDCG@3_diff1(MIRACL)
+      value: 17.134437303330802
+    - type: nAUC_NDCG@3_max(MIRACL)
+      value: 10.235328822955793
+    - type: nAUC_NDCG@3_std(MIRACL)
+      value: 3.2341358691084814
+    - type: nAUC_NDCG@5_diff1(MIRACL)
+      value: 14.733664618337636
+    - type: nAUC_NDCG@5_max(MIRACL)
+      value: 11.181897412035282
+    - type: nAUC_NDCG@5_std(MIRACL)
+      value: 3.642277088791985
+    - type: nAUC_P@1000_diff1(MIRACL)
+      value: -26.330038284867573
+    - type: nAUC_P@1000_max(MIRACL)
+      value: 28.450694137240458
+    - type: nAUC_P@1000_std(MIRACL)
+      value: 9.892993775474912
+    - type: nAUC_P@100_diff1(MIRACL)
+      value: -26.330038284867552
+    - type: nAUC_P@100_max(MIRACL)
+      value: 28.45069413724051
+    - type: nAUC_P@100_std(MIRACL)
+      value: 9.892993775474928
+    - type: nAUC_P@10_diff1(MIRACL)
+      value: -17.436937353231112
+    - type: nAUC_P@10_max(MIRACL)
+      value: 24.327018012947857
+    - type: nAUC_P@10_std(MIRACL)
+      value: 11.78803527706634
+    - type: nAUC_P@1_diff1(MIRACL)
+      value: 19.1388712233292
+    - type: nAUC_P@1_max(MIRACL)
+      value: 10.871900994781642
+    - type: nAUC_P@1_std(MIRACL)
+      value: 3.218568248751811
+    - type: nAUC_P@20_diff1(MIRACL)
+      value: -22.947528755272426
+    - type: nAUC_P@20_max(MIRACL)
+      value: 27.773093471902538
+    - type: nAUC_P@20_std(MIRACL)
+      value: 14.898619107087221
+    - type: nAUC_P@3_diff1(MIRACL)
+      value: 1.4100426412400944
+    - type: nAUC_P@3_max(MIRACL)
+      value: 17.397472872058845
+    - type: nAUC_P@3_std(MIRACL)
+      value: 8.240008229861875
+    - type: nAUC_P@5_diff1(MIRACL)
+      value: -7.971349332207021
+    - type: nAUC_P@5_max(MIRACL)
+      value: 22.198441167940963
+    - type: nAUC_P@5_std(MIRACL)
+      value: 9.00265164460082
+    - type: nAUC_Recall@1000_diff1(MIRACL)
+      value: -38.69835271863148
+    - type: nAUC_Recall@1000_max(MIRACL)
+      value: 50.9545152809108
+    - type: nAUC_Recall@1000_std(MIRACL)
+      value: 20.44270887092116
+    - type: nAUC_Recall@100_diff1(MIRACL)
+      value: -38.69835271863148
+    - type: nAUC_Recall@100_max(MIRACL)
+      value: 50.9545152809108
+    - type: nAUC_Recall@100_std(MIRACL)
+      value: 20.44270887092116
+    - type: nAUC_Recall@10_diff1(MIRACL)
+      value: -0.08109036309433801
+    - type: nAUC_Recall@10_max(MIRACL)
+      value: 12.696619907773568
+    - type: nAUC_Recall@10_std(MIRACL)
+      value: 8.791982704261589
+    - type: nAUC_Recall@1_diff1(MIRACL)
+      value: 28.698973487482206
+    - type: nAUC_Recall@1_max(MIRACL)
+      value: 2.9217687660885034
+    - type: nAUC_Recall@1_std(MIRACL)
+      value: -1.1247408800976524
+    - type: nAUC_Recall@20_diff1(MIRACL)
+      value: -13.312171017942623
+    - type: nAUC_Recall@20_max(MIRACL)
+      value: 24.19847346821666
+    - type: nAUC_Recall@20_std(MIRACL)
+      value: 15.8157702609797
+    - type: nAUC_Recall@3_diff1(MIRACL)
+      value: 16.909128321353343
+    - type: nAUC_Recall@3_max(MIRACL)
+      value: 6.552122731902991
+    - type: nAUC_Recall@3_std(MIRACL)
+      value: 1.9963898223457228
+    - type: nAUC_Recall@5_diff1(MIRACL)
+      value: 9.990292655247721
+    - type: nAUC_Recall@5_max(MIRACL)
+      value: 9.361722273507574
+    - type: nAUC_Recall@5_std(MIRACL)
+      value: 3.270918827854495
+    task:
+      type: Reranking
+  
+  - dataset:
+      config: default
+      name: MTEB SensitiveTopicsClassification (default)
+      revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
+      split: test
+      type: ai-forever/sensitive-topics-classification
+    metrics:
+    - type: accuracy
+      value: 30.634765625
+    - type: f1
+      value: 32.647559808678665
+    - type: lrap
+      value: 45.94319661458259
+    - type: main_score
+      value: 30.634765625
+    task:
+      type: MultilabelClassification
+  - dataset:
+      config: default
+      name: MTEB ATEC (default)
+      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
+      split: test
+      type: C-MTEB/ATEC
+    metrics:
+    - type: cosine_pearson
+      value: 47.541497334563296
+    - type: cosine_spearman
+      value: 49.06268944206629
+    - type: euclidean_pearson
+      value: 51.838926748581635
+    - type: euclidean_spearman
+      value: 48.930697157135356
+    - type: main_score
+      value: 49.06268944206629
+    - type: manhattan_pearson
+      value: 51.835306769406365
+    - type: manhattan_spearman
+      value: 48.86135493444834
+    - type: pearson
+      value: 47.541497334563296
+    - type: spearman
+      value: 49.06268944206629
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB AllegroReviews (default)
+      revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
+      split: test
+      type: PL-MTEB/allegro-reviews
+    metrics:
+    - type: accuracy
+      value: 49.51292246520874
+    - type: f1
+      value: 44.14350234332397
+    - type: f1_weighted
+      value: 51.65508998354552
+    - type: main_score
+      value: 49.51292246520874
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AlloProfClusteringP2P (default)
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+      split: test
+      type: lyon-nlp/alloprof
+    metrics:
+    - type: main_score
+      value: 63.883383458621665
+    - type: v_measure
+      value: 63.883383458621665
+    - type: v_measure_std
+      value: 2.693666879958465
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB 8TagsClustering
+      revision: None
+      split: test
+      type: PL-MTEB/8tags-clustering
+    metrics:
+    - type: v_measure
+      value: 43.657212124525546
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AlloProfClusteringS2S (default)
+      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
+      split: test
+      type: lyon-nlp/alloprof
+    metrics:
+    - type: main_score
+      value: 46.85924588755251
+    - type: v_measure
+      value: 46.85924588755251
+    - type: v_measure_std
+      value: 2.1918258880872377
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AlloprofReranking (default)
+      revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
+      split: test
+      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
+    metrics:
+    - type: map
+      value: 66.39013753839347
+    - type: mrr
+      value: 67.68045617786551
+    - type: main_score
+      value: 66.39013753839347
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB AlloprofRetrieval (default)
+      revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
+      split: test
+      type: lyon-nlp/alloprof
+    metrics:
+    - type: main_score
+      value: 54.284
+    - type: map_at_1
+      value: 37.047000000000004
+    - type: map_at_10
+      value: 48.53
+    - type: map_at_100
+      value: 49.357
+    - type: map_at_1000
+      value: 49.39
+    - type: map_at_20
+      value: 49.064
+    - type: map_at_3
+      value: 45.675
+    - type: map_at_5
+      value: 47.441
+    - type: mrr_at_1
+      value: 37.04663212435233
+    - type: mrr_at_10
+      value: 48.5300326232969
+    - type: mrr_at_100
+      value: 49.35708199037581
+    - type: mrr_at_1000
+      value: 49.39005824603193
+    - type: mrr_at_20
+      value: 49.06417416464799
+    - type: mrr_at_3
+      value: 45.67501439263105
+    - type: mrr_at_5
+      value: 47.44099021301103
+    - type: nauc_map_at_1000_diff1
+      value: 43.32474221868009
+    - type: nauc_map_at_1000_max
+      value: 39.407334029058575
+    - type: nauc_map_at_1000_std
+      value: -2.3728154448932606
+    - type: nauc_map_at_100_diff1
+      value: 43.32336300929909
+    - type: nauc_map_at_100_max
+      value: 39.432174777554835
+    - type: nauc_map_at_100_std
+      value: -2.356396922384349
+    - type: nauc_map_at_10_diff1
+      value: 43.1606520154482
+    - type: nauc_map_at_10_max
+      value: 39.33734650558226
+    - type: nauc_map_at_10_std
+      value: -2.5156222475075256
+    - type: nauc_map_at_1_diff1
+      value: 46.2178975214499
+    - type: nauc_map_at_1_max
+      value: 36.26173199049361
+    - type: nauc_map_at_1_std
+      value: -3.0897555582816443
+    - type: nauc_map_at_20_diff1
+      value: 43.272980702916456
+    - type: nauc_map_at_20_max
+      value: 39.4896977052276
+    - type: nauc_map_at_20_std
+      value: -2.3305501742917043
+    - type: nauc_map_at_3_diff1
+      value: 43.49525042967079
+    - type: nauc_map_at_3_max
+      value: 38.66352501824728
+    - type: nauc_map_at_3_std
+      value: -3.202794391620473
+    - type: nauc_map_at_5_diff1
+      value: 43.2266692546611
+    - type: nauc_map_at_5_max
+      value: 38.77368661115743
+    - type: nauc_map_at_5_std
+      value: -3.0897532130127954
+    - type: nauc_mrr_at_1000_diff1
+      value: 43.32474221868009
+    - type: nauc_mrr_at_1000_max
+      value: 39.407334029058575
+    - type: nauc_mrr_at_1000_std
+      value: -2.3728154448932606
+    - type: nauc_mrr_at_100_diff1
+      value: 43.32336300929909
+    - type: nauc_mrr_at_100_max
+      value: 39.432174777554835
+    - type: nauc_mrr_at_100_std
+      value: -2.356396922384349
+    - type: nauc_mrr_at_10_diff1
+      value: 43.1606520154482
+    - type: nauc_mrr_at_10_max
+      value: 39.33734650558226
+    - type: nauc_mrr_at_10_std
+      value: -2.5156222475075256
+    - type: nauc_mrr_at_1_diff1
+      value: 46.2178975214499
+    - type: nauc_mrr_at_1_max
+      value: 36.26173199049361
+    - type: nauc_mrr_at_1_std
+      value: -3.0897555582816443
+    - type: nauc_mrr_at_20_diff1
+      value: 43.272980702916456
+    - type: nauc_mrr_at_20_max
+      value: 39.4896977052276
+    - type: nauc_mrr_at_20_std
+      value: -2.3305501742917043
+    - type: nauc_mrr_at_3_diff1
+      value: 43.49525042967079
+    - type: nauc_mrr_at_3_max
+      value: 38.66352501824728
+    - type: nauc_mrr_at_3_std
+      value: -3.202794391620473
+    - type: nauc_mrr_at_5_diff1
+      value: 43.2266692546611
+    - type: nauc_mrr_at_5_max
+      value: 38.77368661115743
+    - type: nauc_mrr_at_5_std
+      value: -3.0897532130127954
+    - type: nauc_ndcg_at_1000_diff1
+      value: 43.01903168202974
+    - type: nauc_ndcg_at_1000_max
+      value: 40.75496622942232
+    - type: nauc_ndcg_at_1000_std
+      value: -1.3150412981845496
+    - type: nauc_ndcg_at_100_diff1
+      value: 42.98016493758145
+    - type: nauc_ndcg_at_100_max
+      value: 41.55869635162325
+    - type: nauc_ndcg_at_100_std
+      value: -0.5355252976886055
+    - type: nauc_ndcg_at_10_diff1
+      value: 42.218755211347506
+    - type: nauc_ndcg_at_10_max
+      value: 41.305042275175765
+    - type: nauc_ndcg_at_10_std
+      value: -1.4034484444573714
+    - type: nauc_ndcg_at_1_diff1
+      value: 46.2178975214499
+    - type: nauc_ndcg_at_1_max
+      value: 36.26173199049361
+    - type: nauc_ndcg_at_1_std
+      value: -3.0897555582816443
+    - type: nauc_ndcg_at_20_diff1
+      value: 42.66574440095576
+    - type: nauc_ndcg_at_20_max
+      value: 42.014620115124515
+    - type: nauc_ndcg_at_20_std
+      value: -0.5176162553751498
+    - type: nauc_ndcg_at_3_diff1
+      value: 42.837450505106055
+    - type: nauc_ndcg_at_3_max
+      value: 39.525369733082414
+    - type: nauc_ndcg_at_3_std
+      value: -3.1605948245795155
+    - type: nauc_ndcg_at_5_diff1
+      value: 42.37951815451173
+    - type: nauc_ndcg_at_5_max
+      value: 39.78840132935179
+    - type: nauc_ndcg_at_5_std
+      value: -2.936898430768135
+    - type: nauc_precision_at_1000_diff1
+      value: 49.69224988612385
+    - type: nauc_precision_at_1000_max
+      value: 79.57897547128005
+    - type: nauc_precision_at_1000_std
+      value: 45.040371354764645
+    - type: nauc_precision_at_100_diff1
+      value: 42.70597486048422
+    - type: nauc_precision_at_100_max
+      value: 65.74628759606188
+    - type: nauc_precision_at_100_std
+      value: 25.49157745244855
+    - type: nauc_precision_at_10_diff1
+      value: 38.565609931689345
+    - type: nauc_precision_at_10_max
+      value: 50.0239696180852
+    - type: nauc_precision_at_10_std
+      value: 3.976354829503967
+    - type: nauc_precision_at_1_diff1
+      value: 46.2178975214499
+    - type: nauc_precision_at_1_max
+      value: 36.26173199049361
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+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
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+      name: MTEB AmazonCounterfactualClassification (de)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
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+      name: MTEB AmazonPolarityClassification (default)
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
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+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
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+      name: MTEB AmazonReviewsClassification (de)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
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+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
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+      split: test
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+    - type: nauc_precision_at_1_diff1
+      value: 19.433947086968093
+    - type: nauc_precision_at_1_max
+      value: 24.75657047550517
+    - type: nauc_precision_at_1_std
+      value: 15.122329157218505
+    - type: nauc_precision_at_20_diff1
+      value: 12.018814361204328
+    - type: nauc_precision_at_20_max
+      value: 19.75123746049928
+    - type: nauc_precision_at_20_std
+      value: 3.012204650582264
+    - type: nauc_precision_at_3_diff1
+      value: 17.41375604940955
+    - type: nauc_precision_at_3_max
+      value: 23.699834627021037
+    - type: nauc_precision_at_3_std
+      value: 6.793486779050103
+    - type: nauc_precision_at_5_diff1
+      value: 19.194631963780257
+    - type: nauc_precision_at_5_max
+      value: 23.31708702442155
+    - type: nauc_precision_at_5_std
+      value: 3.4591358279667332
+    - type: nauc_recall_at_1000_diff1
+      value: 7.919843339380378
+    - type: nauc_recall_at_1000_max
+      value: 31.57538623427063
+    - type: nauc_recall_at_1000_std
+      value: 39.332224386769546
+    - type: nauc_recall_at_100_diff1
+      value: 15.018050960000085
+    - type: nauc_recall_at_100_max
+      value: 34.9820951375986
+    - type: nauc_recall_at_100_std
+      value: 26.97003448435901
+    - type: nauc_recall_at_10_diff1
+      value: 12.102191084210837
+    - type: nauc_recall_at_10_max
+      value: 18.112541150340594
+    - type: nauc_recall_at_10_std
+      value: 0.7358784689405188
+    - type: nauc_recall_at_1_diff1
+      value: 19.433947086968093
+    - type: nauc_recall_at_1_max
+      value: 24.75657047550517
+    - type: nauc_recall_at_1_std
+      value: 15.122329157218505
+    - type: nauc_recall_at_20_diff1
+      value: 12.01881436120429
+    - type: nauc_recall_at_20_max
+      value: 19.751237460499222
+    - type: nauc_recall_at_20_std
+      value: 3.0122046505822135
+    - type: nauc_recall_at_3_diff1
+      value: 17.413756049409503
+    - type: nauc_recall_at_3_max
+      value: 23.699834627020998
+    - type: nauc_recall_at_3_std
+      value: 6.793486779050083
+    - type: nauc_recall_at_5_diff1
+      value: 19.194631963780203
+    - type: nauc_recall_at_5_max
+      value: 23.3170870244215
+    - type: nauc_recall_at_5_std
+      value: 3.459135827966664
+    - type: ndcg_at_1
+      value: 14.865
+    - type: ndcg_at_10
+      value: 24.764
+    - type: ndcg_at_100
+      value: 30.861
+    - type: ndcg_at_1000
+      value: 33.628
+    - type: ndcg_at_20
+      value: 27.078000000000003
+    - type: ndcg_at_3
+      value: 21.675
+    - type: ndcg_at_5
+      value: 23.148
+    - type: precision_at_1
+      value: 14.865
+    - type: precision_at_10
+      value: 3.4680000000000004
+    - type: precision_at_100
+      value: 0.644
+    - type: precision_at_1000
+      value: 0.087
+    - type: precision_at_20
+      value: 2.185
+    - type: precision_at_3
+      value: 8.709
+    - type: precision_at_5
+      value: 5.946
+    - type: recall_at_1
+      value: 14.865
+    - type: recall_at_10
+      value: 34.685
+    - type: recall_at_100
+      value: 64.414
+    - type: recall_at_1000
+      value: 86.937
+    - type: recall_at_20
+      value: 43.694
+    - type: recall_at_3
+      value: 26.125999999999998
+    - type: recall_at_5
+      value: 29.73
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification (default)
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 84.08116883116882
+    - type: f1
+      value: 84.05587055990273
+    - type: f1_weighted
+      value: 84.05587055990274
+    - type: main_score
+      value: 84.08116883116882
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P (default)
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: main_score
+      value: 38.1941007822277
+    - type: v_measure
+      value: 38.1941007822277
+    - type: v_measure_std
+      value: 0.7502113547288178
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S (default)
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: main_score
+      value: 34.42075599178318
+    - type: v_measure
+      value: 34.42075599178318
+    - type: v_measure_std
+      value: 0.600256720497283
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BlurbsClusteringP2P (default)
+      revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
+      split: test
+      type: slvnwhrl/blurbs-clustering-p2p
+    metrics:
+    - type: main_score
+      value: 41.634627363047265
+    - type: v_measure
+      value: 41.634627363047265
+    - type: v_measure_std
+      value: 9.726923191225307
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BlurbsClusteringS2S (default)
+      revision: 22793b6a6465bf00120ad525e38c51210858132c
+      split: test
+      type: slvnwhrl/blurbs-clustering-s2s
+    metrics:
+    - type: main_score
+      value: 20.996468295584197
+    - type: v_measure
+      value: 20.996468295584197
+    - type: v_measure_std
+      value: 9.225766688272197
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CBD (default)
+      revision: 36ddb419bcffe6a5374c3891957912892916f28d
+      split: test
+      type: PL-MTEB/cbd
+    metrics:
+    - type: accuracy
+      value: 69.99
+    - type: ap
+      value: 22.57826353116948
+    - type: ap_weighted
+      value: 22.57826353116948
+    - type: f1
+      value: 59.04574955548393
+    - type: f1_weighted
+      value: 74.36235022309789
+    - type: main_score
+      value: 69.99
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB CDSC-E (default)
+      revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
+      split: test
+      type: PL-MTEB/cdsce-pairclassification
+    metrics:
+    - type: cosine_accuracy
+      value: 88.7
+    - type: cosine_accuracy_threshold
+      value: 97.37848043441772
+    - type: cosine_ap
+      value: 73.0405088928302
+    - type: cosine_f1
+      value: 63.52201257861635
+    - type: cosine_f1_threshold
+      value: 96.98888063430786
+    - type: cosine_precision
+      value: 78.90625
+    - type: cosine_recall
+      value: 53.1578947368421
+    - type: dot_accuracy
+      value: 84.89999999999999
+    - type: dot_accuracy_threshold
+      value: 43603.09753417969
+    - type: dot_ap
+      value: 56.98157569085279
+    - type: dot_f1
+      value: 57.606490872210955
+    - type: dot_f1_threshold
+      value: 40406.23779296875
+    - type: dot_precision
+      value: 46.864686468646866
+    - type: dot_recall
+      value: 74.73684210526315
+    - type: euclidean_accuracy
+      value: 88.5
+    - type: euclidean_accuracy_threshold
+      value: 498.0483055114746
+    - type: euclidean_ap
+      value: 72.97328234816734
+    - type: euclidean_f1
+      value: 63.722397476340696
+    - type: euclidean_f1_threshold
+      value: 508.6186408996582
+    - type: euclidean_precision
+      value: 79.52755905511812
+    - type: euclidean_recall
+      value: 53.1578947368421
+    - type: main_score
+      value: 73.0405088928302
+    - type: manhattan_accuracy
+      value: 88.6
+    - type: manhattan_accuracy_threshold
+      value: 12233.079528808594
+    - type: manhattan_ap
+      value: 72.92148503992615
+    - type: manhattan_f1
+      value: 63.69426751592356
+    - type: manhattan_f1_threshold
+      value: 12392.754364013672
+    - type: manhattan_precision
+      value: 80.64516129032258
+    - type: manhattan_recall
+      value: 52.63157894736842
+    - type: max_accuracy
+      value: 88.7
+    - type: max_ap
+      value: 73.0405088928302
+    - type: max_f1
+      value: 63.722397476340696
+    - type: max_precision
+      value: 80.64516129032258
+    - type: max_recall
+      value: 74.73684210526315
+    - type: similarity_accuracy
+      value: 88.7
+    - type: similarity_accuracy_threshold
+      value: 97.37848043441772
+    - type: similarity_ap
+      value: 73.0405088928302
+    - type: similarity_f1
+      value: 63.52201257861635
+    - type: similarity_f1_threshold
+      value: 96.98888063430786
+    - type: similarity_precision
+      value: 78.90625
+    - type: similarity_recall
+      value: 53.1578947368421
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB CDSC-R (default)
+      revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
+      split: test
+      type: PL-MTEB/cdscr-sts
+    metrics:
+    - type: cosine_pearson
+      value: 92.97492495289738
+    - type: cosine_spearman
+      value: 92.63248098608472
+    - type: euclidean_pearson
+      value: 92.04712487782031
+    - type: euclidean_spearman
+      value: 92.19679486755008
+    - type: main_score
+      value: 92.63248098608472
+    - type: manhattan_pearson
+      value: 92.0101187740438
+    - type: manhattan_spearman
+      value: 92.20926859332754
+    - type: pearson
+      value: 92.97492495289738
+    - type: spearman
+      value: 92.63248098608472
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB CLSClusteringP2P (default)
+      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
+      split: test
+      type: C-MTEB/CLSClusteringP2P
+    metrics:
+    - type: main_score
+      value: 39.96377851800628
+    - type: v_measure
+      value: 39.96377851800628
+    - type: v_measure_std
+      value: 0.9793033243093288
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CLSClusteringS2S (default)
+      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
+      split: test
+      type: C-MTEB/CLSClusteringS2S
+    metrics:
+    - type: main_score
+      value: 38.788850224595784
+    - type: v_measure
+      value: 38.788850224595784
+    - type: v_measure_std
+      value: 1.0712604145916924
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CMedQAv1
+      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
+      split: test
+      type: C-MTEB/CMedQAv1-reranking
+    metrics:
+    - type: map
+      value: 77.95952507806115
+    - type: mrr
+      value: 80.8643253968254
+    - type: main_score
+      value: 77.95952507806115
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB CMedQAv2
+      revision: 23d186750531a14a0357ca22cd92d712fd512ea0
+      split: test
+      type: C-MTEB/CMedQAv2-reranking
+    metrics:
+    - type: map
+      value: 78.21522500165045
+    - type: mrr
+      value: 81.28194444444443
+    - type: main_score
+      value: 78.21522500165045
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval (default)
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+      split: test
+      type: mteb/cqadupstack-android
+    metrics:
+    - type: map_at_1
+      value: 33.377
+    - type: map_at_10
+      value: 46.371
+    - type: map_at_100
+      value: 47.829
+    - type: map_at_1000
+      value: 47.94
+    - type: map_at_20
+      value: 47.205000000000005
+    - type: map_at_3
+      value: 42.782
+    - type: map_at_5
+      value: 44.86
+    - type: mrr_at_1
+      value: 41.345
+    - type: mrr_at_10
+      value: 52.187
+    - type: mrr_at_100
+      value: 52.893
+    - type: mrr_at_1000
+      value: 52.929
+    - type: mrr_at_20
+      value: 52.637
+    - type: mrr_at_3
+      value: 49.714000000000006
+    - type: mrr_at_5
+      value: 51.373000000000005
+    - type: ndcg_at_1
+      value: 41.345
+    - type: ndcg_at_10
+      value: 52.946000000000005
+    - type: ndcg_at_100
+      value: 57.92699999999999
+    - type: ndcg_at_1000
+      value: 59.609
+    - type: ndcg_at_20
+      value: 54.900999999999996
+    - type: ndcg_at_3
+      value: 48.357
+    - type: ndcg_at_5
+      value: 50.739000000000004
+    - type: precision_at_1
+      value: 41.345
+    - type: precision_at_10
+      value: 10.186
+    - type: precision_at_100
+      value: 1.554
+    - type: precision_at_1000
+      value: 0.2
+    - type: precision_at_20
+      value: 5.959
+    - type: precision_at_3
+      value: 23.796
+    - type: precision_at_5
+      value: 17.024
+    - type: recall_at_1
+      value: 33.377
+    - type: recall_at_10
+      value: 65.067
+    - type: recall_at_100
+      value: 86.04899999999999
+    - type: recall_at_1000
+      value: 96.54899999999999
+    - type: recall_at_20
+      value: 72.071
+    - type: recall_at_3
+      value: 51.349999999999994
+    - type: recall_at_5
+      value: 58.41
+    - type: main_score
+      value: 52.946000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackEnglishRetrieval (default)
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+      split: test
+      type: mteb/cqadupstack-english
+    metrics:
+    - type: map_at_1
+      value: 31.097
+    - type: map_at_10
+      value: 42.183
+    - type: map_at_100
+      value: 43.580999999999996
+    - type: map_at_1000
+      value: 43.718
+    - type: map_at_20
+      value: 42.921
+    - type: map_at_3
+      value: 38.963
+    - type: map_at_5
+      value: 40.815
+    - type: mrr_at_1
+      value: 39.745000000000005
+    - type: mrr_at_10
+      value: 48.736000000000004
+    - type: mrr_at_100
+      value: 49.405
+    - type: mrr_at_1000
+      value: 49.452
+    - type: mrr_at_20
+      value: 49.118
+    - type: mrr_at_3
+      value: 46.497
+    - type: mrr_at_5
+      value: 47.827999999999996
+    - type: ndcg_at_1
+      value: 39.745000000000005
+    - type: ndcg_at_10
+      value: 48.248000000000005
+    - type: ndcg_at_100
+      value: 52.956
+    - type: ndcg_at_1000
+      value: 54.99699999999999
+    - type: ndcg_at_20
+      value: 50.01
+    - type: ndcg_at_3
+      value: 43.946000000000005
+    - type: ndcg_at_5
+      value: 46.038000000000004
+    - type: precision_at_1
+      value: 39.745000000000005
+    - type: precision_at_10
+      value: 9.229
+    - type: precision_at_100
+      value: 1.5070000000000001
+    - type: precision_at_1000
+      value: 0.199
+    - type: precision_at_20
+      value: 5.489999999999999
+    - type: precision_at_3
+      value: 21.38
+    - type: precision_at_5
+      value: 15.274
+    - type: recall_at_1
+      value: 31.097
+    - type: recall_at_10
+      value: 58.617
+    - type: recall_at_100
+      value: 78.55199999999999
+    - type: recall_at_1000
+      value: 91.13900000000001
+    - type: recall_at_20
+      value: 64.92
+    - type: recall_at_3
+      value: 45.672000000000004
+    - type: recall_at_5
+      value: 51.669
+    - type: main_score
+      value: 48.248000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGamingRetrieval (default)
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+      split: test
+      type: mteb/cqadupstack-gaming
+    metrics:
+    - type: map_at_1
+      value: 39.745000000000005
+    - type: map_at_10
+      value: 52.063
+    - type: map_at_100
+      value: 53.077
+    - type: map_at_1000
+      value: 53.13
+    - type: map_at_20
+      value: 52.66
+    - type: map_at_3
+      value: 48.662
+    - type: map_at_5
+      value: 50.507000000000005
+    - type: mrr_at_1
+      value: 45.391999999999996
+    - type: mrr_at_10
+      value: 55.528
+    - type: mrr_at_100
+      value: 56.16100000000001
+    - type: mrr_at_1000
+      value: 56.192
+    - type: mrr_at_20
+      value: 55.923
+    - type: mrr_at_3
+      value: 52.93600000000001
+    - type: mrr_at_5
+      value: 54.435
+    - type: ndcg_at_1
+      value: 45.391999999999996
+    - type: ndcg_at_10
+      value: 58.019
+    - type: ndcg_at_100
+      value: 61.936
+    - type: ndcg_at_1000
+      value: 63.015
+    - type: ndcg_at_20
+      value: 59.691
+    - type: ndcg_at_3
+      value: 52.294
+    - type: ndcg_at_5
+      value: 55.017
+    - type: precision_at_1
+      value: 45.391999999999996
+    - type: precision_at_10
+      value: 9.386
+    - type: precision_at_100
+      value: 1.232
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_20
+      value: 5.223
+    - type: precision_at_3
+      value: 23.177
+    - type: precision_at_5
+      value: 15.9
+    - type: recall_at_1
+      value: 39.745000000000005
+    - type: recall_at_10
+      value: 72.08099999999999
+    - type: recall_at_100
+      value: 88.85300000000001
+    - type: recall_at_1000
+      value: 96.569
+    - type: recall_at_20
+      value: 78.203
+    - type: recall_at_3
+      value: 56.957
+    - type: recall_at_5
+      value: 63.63100000000001
+    - type: main_score
+      value: 58.019
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGisRetrieval (default)
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+      split: test
+      type: mteb/cqadupstack-gis
+    metrics:
+    - type: map_at_1
+      value: 26.651999999999997
+    - type: map_at_10
+      value: 35.799
+    - type: map_at_100
+      value: 36.846000000000004
+    - type: map_at_1000
+      value: 36.931000000000004
+    - type: map_at_20
+      value: 36.341
+    - type: map_at_3
+      value: 32.999
+    - type: map_at_5
+      value: 34.597
+    - type: mrr_at_1
+      value: 28.814
+    - type: mrr_at_10
+      value: 37.869
+    - type: mrr_at_100
+      value: 38.728
+    - type: mrr_at_1000
+      value: 38.795
+    - type: mrr_at_20
+      value: 38.317
+    - type: mrr_at_3
+      value: 35.235
+    - type: mrr_at_5
+      value: 36.738
+    - type: ndcg_at_1
+      value: 28.814
+    - type: ndcg_at_10
+      value: 41.028
+    - type: ndcg_at_100
+      value: 46.162
+    - type: ndcg_at_1000
+      value: 48.15
+    - type: ndcg_at_20
+      value: 42.824
+    - type: ndcg_at_3
+      value: 35.621
+    - type: ndcg_at_5
+      value: 38.277
+    - type: precision_at_1
+      value: 28.814
+    - type: precision_at_10
+      value: 6.361999999999999
+    - type: precision_at_100
+      value: 0.9450000000000001
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_20
+      value: 3.6159999999999997
+    - type: precision_at_3
+      value: 15.140999999999998
+    - type: precision_at_5
+      value: 10.712000000000002
+    - type: recall_at_1
+      value: 26.651999999999997
+    - type: recall_at_10
+      value: 55.038
+    - type: recall_at_100
+      value: 78.806
+    - type: recall_at_1000
+      value: 93.485
+    - type: recall_at_20
+      value: 61.742
+    - type: recall_at_3
+      value: 40.682
+    - type: recall_at_5
+      value: 46.855000000000004
+    - type: main_score
+      value: 41.028
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackMathematicaRetrieval (default)
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+      split: test
+      type: mteb/cqadupstack-mathematica
+    metrics:
+    - type: map_at_1
+      value: 17.627000000000002
+    - type: map_at_10
+      value: 26.436999999999998
+    - type: map_at_100
+      value: 27.85
+    - type: map_at_1000
+      value: 27.955999999999996
+    - type: map_at_20
+      value: 27.233
+    - type: map_at_3
+      value: 23.777
+    - type: map_at_5
+      value: 25.122
+    - type: mrr_at_1
+      value: 22.387999999999998
+    - type: mrr_at_10
+      value: 31.589
+    - type: mrr_at_100
+      value: 32.641999999999996
+    - type: mrr_at_1000
+      value: 32.696999999999996
+    - type: mrr_at_20
+      value: 32.201
+    - type: mrr_at_3
+      value: 28.98
+    - type: mrr_at_5
+      value: 30.342000000000002
+    - type: ndcg_at_1
+      value: 22.387999999999998
+    - type: ndcg_at_10
+      value: 32.129999999999995
+    - type: ndcg_at_100
+      value: 38.562999999999995
+    - type: ndcg_at_1000
+      value: 40.903
+    - type: ndcg_at_20
+      value: 34.652
+    - type: ndcg_at_3
+      value: 27.26
+    - type: ndcg_at_5
+      value: 29.235
+    - type: precision_at_1
+      value: 22.387999999999998
+    - type: precision_at_10
+      value: 5.970000000000001
+    - type: precision_at_100
+      value: 1.068
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_20
+      value: 3.6999999999999997
+    - type: precision_at_3
+      value: 13.267000000000001
+    - type: precision_at_5
+      value: 9.403
+    - type: recall_at_1
+      value: 17.627000000000002
+    - type: recall_at_10
+      value: 44.71
+    - type: recall_at_100
+      value: 72.426
+    - type: recall_at_1000
+      value: 88.64699999999999
+    - type: recall_at_20
+      value: 53.65
+    - type: recall_at_3
+      value: 30.989
+    - type: recall_at_5
+      value: 36.237
+    - type: main_score
+      value: 32.129999999999995
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackPhysicsRetrieval (default)
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+      split: test
+      type: mteb/cqadupstack-physics
+    metrics:
+    - type: map_at_1
+      value: 30.891000000000002
+    - type: map_at_10
+      value: 41.519
+    - type: map_at_100
+      value: 42.896
+    - type: map_at_1000
+      value: 42.992999999999995
+    - type: map_at_20
+      value: 42.287
+    - type: map_at_3
+      value: 37.822
+    - type: map_at_5
+      value: 39.976
+    - type: mrr_at_1
+      value: 37.921
+    - type: mrr_at_10
+      value: 47.260999999999996
+    - type: mrr_at_100
+      value: 48.044
+    - type: mrr_at_1000
+      value: 48.08
+    - type: mrr_at_20
+      value: 47.699999999999996
+    - type: mrr_at_3
+      value: 44.513999999999996
+    - type: mrr_at_5
+      value: 46.064
+    - type: ndcg_at_1
+      value: 37.921
+    - type: ndcg_at_10
+      value: 47.806
+    - type: ndcg_at_100
+      value: 53.274
+    - type: ndcg_at_1000
+      value: 55.021
+    - type: ndcg_at_20
+      value: 49.973
+    - type: ndcg_at_3
+      value: 42.046
+    - type: ndcg_at_5
+      value: 44.835
+    - type: precision_at_1
+      value: 37.921
+    - type: precision_at_10
+      value: 8.767999999999999
+    - type: precision_at_100
+      value: 1.353
+    - type: precision_at_1000
+      value: 0.168
+    - type: precision_at_20
+      value: 5.135
+    - type: precision_at_3
+      value: 20.051
+    - type: precision_at_5
+      value: 14.398
+    - type: recall_at_1
+      value: 30.891000000000002
+    - type: recall_at_10
+      value: 60.897999999999996
+    - type: recall_at_100
+      value: 83.541
+    - type: recall_at_1000
+      value: 94.825
+    - type: recall_at_20
+      value: 68.356
+    - type: recall_at_3
+      value: 44.65
+    - type: recall_at_5
+      value: 51.919000000000004
+    - type: main_score
+      value: 47.806
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackProgrammersRetrieval (default)
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+      split: test
+      type: mteb/cqadupstack-programmers
+    metrics:
+    - type: map_at_1
+      value: 27.654
+    - type: map_at_10
+      value: 38.025999999999996
+    - type: map_at_100
+      value: 39.425
+    - type: map_at_1000
+      value: 39.528
+    - type: map_at_20
+      value: 38.838
+    - type: map_at_3
+      value: 34.745
+    - type: map_at_5
+      value: 36.537
+    - type: mrr_at_1
+      value: 34.018
+    - type: mrr_at_10
+      value: 43.314
+    - type: mrr_at_100
+      value: 44.283
+    - type: mrr_at_1000
+      value: 44.327
+    - type: mrr_at_20
+      value: 43.929
+    - type: mrr_at_3
+      value: 40.868
+    - type: mrr_at_5
+      value: 42.317
+    - type: ndcg_at_1
+      value: 34.018
+    - type: ndcg_at_10
+      value: 43.887
+    - type: ndcg_at_100
+      value: 49.791000000000004
+    - type: ndcg_at_1000
+      value: 51.834
+    - type: ndcg_at_20
+      value: 46.376
+    - type: ndcg_at_3
+      value: 38.769999999999996
+    - type: ndcg_at_5
+      value: 41.144
+    - type: precision_at_1
+      value: 34.018
+    - type: precision_at_10
+      value: 8.001999999999999
+    - type: precision_at_100
+      value: 1.2630000000000001
+    - type: precision_at_1000
+      value: 0.16
+    - type: precision_at_20
+      value: 4.737
+    - type: precision_at_3
+      value: 18.417
+    - type: precision_at_5
+      value: 13.150999999999998
+    - type: recall_at_1
+      value: 27.654
+    - type: recall_at_10
+      value: 56.111
+    - type: recall_at_100
+      value: 81.136
+    - type: recall_at_1000
+      value: 94.788
+    - type: recall_at_20
+      value: 65.068
+    - type: recall_at_3
+      value: 41.713
+    - type: recall_at_5
+      value: 48.106
+    - type: main_score
+      value: 43.887
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackRetrieval (default)
+      revision: CQADupstackRetrieval_is_a_combined_dataset
+      split: test
+      type: CQADupstackRetrieval_is_a_combined_dataset
+    metrics:
+    - type: main_score
+      value: 42.58858333333333
+    - type: ndcg_at_10
+      value: 42.58858333333333
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackStatsRetrieval (default)
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+      split: test
+      type: mteb/cqadupstack-stats
+    metrics:
+    - type: map_at_1
+      value: 24.501
+    - type: map_at_10
+      value: 32.814
+    - type: map_at_100
+      value: 33.754
+    - type: map_at_1000
+      value: 33.859
+    - type: map_at_20
+      value: 33.324
+    - type: map_at_3
+      value: 30.758000000000003
+    - type: map_at_5
+      value: 31.936999999999998
+    - type: mrr_at_1
+      value: 27.761000000000003
+    - type: mrr_at_10
+      value: 35.662
+    - type: mrr_at_100
+      value: 36.443999999999996
+    - type: mrr_at_1000
+      value: 36.516999999999996
+    - type: mrr_at_20
+      value: 36.085
+    - type: mrr_at_3
+      value: 33.742
+    - type: mrr_at_5
+      value: 34.931
+    - type: ndcg_at_1
+      value: 27.761000000000003
+    - type: ndcg_at_10
+      value: 37.208000000000006
+    - type: ndcg_at_100
+      value: 41.839
+    - type: ndcg_at_1000
+      value: 44.421
+    - type: ndcg_at_20
+      value: 38.917
+    - type: ndcg_at_3
+      value: 33.544000000000004
+    - type: ndcg_at_5
+      value: 35.374
+    - type: precision_at_1
+      value: 27.761000000000003
+    - type: precision_at_10
+      value: 5.92
+    - type: precision_at_100
+      value: 0.899
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_20
+      value: 3.4130000000000003
+    - type: precision_at_3
+      value: 15.031
+    - type: precision_at_5
+      value: 10.306999999999999
+    - type: recall_at_1
+      value: 24.501
+    - type: recall_at_10
+      value: 47.579
+    - type: recall_at_100
+      value: 69.045
+    - type: recall_at_1000
+      value: 88.032
+    - type: recall_at_20
+      value: 54.125
+    - type: recall_at_3
+      value: 37.202
+    - type: recall_at_5
+      value: 41.927
+    - type: main_score
+      value: 37.208000000000006
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackTexRetrieval (default)
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+      split: test
+      type: mteb/cqadupstack-tex
+    metrics:
+    - type: map_at_1
+      value: 18.29
+    - type: map_at_10
+      value: 26.183
+    - type: map_at_100
+      value: 27.351999999999997
+    - type: map_at_1000
+      value: 27.483999999999998
+    - type: map_at_20
+      value: 26.798
+    - type: map_at_3
+      value: 23.629
+    - type: map_at_5
+      value: 24.937
+    - type: mrr_at_1
+      value: 22.299
+    - type: mrr_at_10
+      value: 30.189
+    - type: mrr_at_100
+      value: 31.098
+    - type: mrr_at_1000
+      value: 31.177
+    - type: mrr_at_20
+      value: 30.697000000000003
+    - type: mrr_at_3
+      value: 27.862
+    - type: mrr_at_5
+      value: 29.066
+    - type: ndcg_at_1
+      value: 22.299
+    - type: ndcg_at_10
+      value: 31.202
+    - type: ndcg_at_100
+      value: 36.617
+    - type: ndcg_at_1000
+      value: 39.544000000000004
+    - type: ndcg_at_20
+      value: 33.177
+    - type: ndcg_at_3
+      value: 26.639000000000003
+    - type: ndcg_at_5
+      value: 28.526
+    - type: precision_at_1
+      value: 22.299
+    - type: precision_at_10
+      value: 5.8020000000000005
+    - type: precision_at_100
+      value: 1.0070000000000001
+    - type: precision_at_1000
+      value: 0.14400000000000002
+    - type: precision_at_20
+      value: 3.505
+    - type: precision_at_3
+      value: 12.698
+    - type: precision_at_5
+      value: 9.174
+    - type: recall_at_1
+      value: 18.29
+    - type: recall_at_10
+      value: 42.254999999999995
+    - type: recall_at_100
+      value: 66.60000000000001
+    - type: recall_at_1000
+      value: 87.31400000000001
+    - type: recall_at_20
+      value: 49.572
+    - type: recall_at_3
+      value: 29.342000000000002
+    - type: recall_at_5
+      value: 34.221000000000004
+    - type: main_score
+      value: 31.202
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackUnixRetrieval (default)
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+      split: test
+      type: mteb/cqadupstack-unix
+    metrics:
+    - type: map_at_1
+      value: 27.722
+    - type: map_at_10
+      value: 37.698
+    - type: map_at_100
+      value: 38.899
+    - type: map_at_1000
+      value: 38.998
+    - type: map_at_20
+      value: 38.381
+    - type: map_at_3
+      value: 34.244
+    - type: map_at_5
+      value: 36.295
+    - type: mrr_at_1
+      value: 32.183
+    - type: mrr_at_10
+      value: 41.429
+    - type: mrr_at_100
+      value: 42.308
+    - type: mrr_at_1000
+      value: 42.358000000000004
+    - type: mrr_at_20
+      value: 41.957
+    - type: mrr_at_3
+      value: 38.401999999999994
+    - type: mrr_at_5
+      value: 40.294999999999995
+    - type: ndcg_at_1
+      value: 32.183
+    - type: ndcg_at_10
+      value: 43.519000000000005
+    - type: ndcg_at_100
+      value: 48.786
+    - type: ndcg_at_1000
+      value: 50.861999999999995
+    - type: ndcg_at_20
+      value: 45.654
+    - type: ndcg_at_3
+      value: 37.521
+    - type: ndcg_at_5
+      value: 40.615
+    - type: precision_at_1
+      value: 32.183
+    - type: precision_at_10
+      value: 7.603
+    - type: precision_at_100
+      value: 1.135
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_20
+      value: 4.408
+    - type: precision_at_3
+      value: 17.071
+    - type: precision_at_5
+      value: 12.668
+    - type: recall_at_1
+      value: 27.722
+    - type: recall_at_10
+      value: 57.230000000000004
+    - type: recall_at_100
+      value: 79.97999999999999
+    - type: recall_at_1000
+      value: 94.217
+    - type: recall_at_20
+      value: 64.864
+    - type: recall_at_3
+      value: 41.215
+    - type: recall_at_5
+      value: 48.774
+    - type: main_score
+      value: 43.519000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWebmastersRetrieval (default)
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+      split: test
+      type: mteb/cqadupstack-webmasters
+    metrics:
+    - type: map_at_1
+      value: 25.852999999999998
+    - type: map_at_10
+      value: 35.394999999999996
+    - type: map_at_100
+      value: 37.291999999999994
+    - type: map_at_1000
+      value: 37.495
+    - type: map_at_20
+      value: 36.372
+    - type: map_at_3
+      value: 32.336
+    - type: map_at_5
+      value: 34.159
+    - type: mrr_at_1
+      value: 31.818
+    - type: mrr_at_10
+      value: 40.677
+    - type: mrr_at_100
+      value: 41.728
+    - type: mrr_at_1000
+      value: 41.778
+    - type: mrr_at_20
+      value: 41.301
+    - type: mrr_at_3
+      value: 38.208
+    - type: mrr_at_5
+      value: 39.592
+    - type: ndcg_at_1
+      value: 31.818
+    - type: ndcg_at_10
+      value: 41.559000000000005
+    - type: ndcg_at_100
+      value: 48.012
+    - type: ndcg_at_1000
+      value: 50.234
+    - type: ndcg_at_20
+      value: 44.15
+    - type: ndcg_at_3
+      value: 36.918
+    - type: ndcg_at_5
+      value: 39.227000000000004
+    - type: precision_at_1
+      value: 31.818
+    - type: precision_at_10
+      value: 8.043
+    - type: precision_at_100
+      value: 1.625
+    - type: precision_at_1000
+      value: 0.245
+    - type: precision_at_20
+      value: 5.2170000000000005
+    - type: precision_at_3
+      value: 17.655
+    - type: precision_at_5
+      value: 12.845999999999998
+    - type: recall_at_1
+      value: 25.852999999999998
+    - type: recall_at_10
+      value: 53.093
+    - type: recall_at_100
+      value: 81.05799999999999
+    - type: recall_at_1000
+      value: 94.657
+    - type: recall_at_20
+      value: 62.748000000000005
+    - type: recall_at_3
+      value: 39.300000000000004
+    - type: recall_at_5
+      value: 45.754
+    - type: main_score
+      value: 41.559000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWordpressRetrieval (default)
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+      split: test
+      type: mteb/cqadupstack-wordpress
+    metrics:
+    - type: map_at_1
+      value: 19.23
+    - type: map_at_10
+      value: 28.128999999999998
+    - type: map_at_100
+      value: 29.195
+    - type: map_at_1000
+      value: 29.310000000000002
+    - type: map_at_20
+      value: 28.713
+    - type: map_at_3
+      value: 25.191000000000003
+    - type: map_at_5
+      value: 26.69
+    - type: mrr_at_1
+      value: 21.257
+    - type: mrr_at_10
+      value: 30.253999999999998
+    - type: mrr_at_100
+      value: 31.195
+    - type: mrr_at_1000
+      value: 31.270999999999997
+    - type: mrr_at_20
+      value: 30.747999999999998
+    - type: mrr_at_3
+      value: 27.633999999999997
+    - type: mrr_at_5
+      value: 28.937
+    - type: ndcg_at_1
+      value: 21.257
+    - type: ndcg_at_10
+      value: 33.511
+    - type: ndcg_at_100
+      value: 38.733000000000004
+    - type: ndcg_at_1000
+      value: 41.489
+    - type: ndcg_at_20
+      value: 35.476
+    - type: ndcg_at_3
+      value: 27.845
+    - type: ndcg_at_5
+      value: 30.264999999999997
+    - type: precision_at_1
+      value: 21.257
+    - type: precision_at_10
+      value: 5.619
+    - type: precision_at_100
+      value: 0.893
+    - type: precision_at_1000
+      value: 0.124
+    - type: precision_at_20
+      value: 3.29
+    - type: precision_at_3
+      value: 12.508
+    - type: precision_at_5
+      value: 8.946
+    - type: recall_at_1
+      value: 19.23
+    - type: recall_at_10
+      value: 48.185
+    - type: recall_at_100
+      value: 71.932
+    - type: recall_at_1000
+      value: 92.587
+    - type: recall_at_20
+      value: 55.533
+    - type: recall_at_3
+      value: 32.865
+    - type: recall_at_5
+      value: 38.577
+    - type: main_score
+      value: 33.511
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER (default)
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+      split: test
+      type: mteb/climate-fever
+    metrics:
+    - type: map_at_1
+      value: 19.594
+    - type: map_at_10
+      value: 32.519
+    - type: map_at_100
+      value: 34.1
+    - type: map_at_1000
+      value: 34.263
+    - type: map_at_20
+      value: 33.353
+    - type: map_at_3
+      value: 27.898
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+    - type: ndcg_at_1000
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+    - type: ndcg_at_20
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+    - type: recall_at_1000
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+    - type: recall_at_20
+      value: 99.546
+    - type: recall_at_3
+      value: 97.414
+    - type: recall_at_5
+      value: 98.503
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB GermanSTSBenchmark (default)
+      revision: e36907544d44c3a247898ed81540310442329e20
+      split: test
+      type: jinaai/german-STSbenchmark
+    metrics:
+    - type: cosine_pearson
+      value: 86.55319003300265
+    - type: cosine_spearman
+      value: 87.50267373081324
+    - type: euclidean_pearson
+      value: 87.41630636501863
+    - type: euclidean_spearman
+      value: 88.02170803409365
+    - type: main_score
+      value: 87.50267373081324
+    - type: manhattan_pearson
+      value: 87.33703179056744
+    - type: manhattan_spearman
+      value: 87.99192826922514
+    - type: pearson
+      value: 86.55319003300265
+    - type: spearman
+      value: 87.50267373081324
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB HALClusteringS2S (default)
+      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
+      split: test
+      type: lyon-nlp/clustering-hal-s2s
+    metrics:
+    - type: main_score
+      value: 27.477557517301303
+    - type: v_measure
+      value: 27.477557517301303
+    - type: v_measure_std
+      value: 3.3525736581861336
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB HeadlineClassification (default)
+      revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
+      split: test
+      type: ai-forever/headline-classification
+    metrics:
+    - type: accuracy
+      value: 75.0830078125
+    - type: f1
+      value: 75.08863209267814
+    - type: f1_weighted
+      value: 75.08895979060917
+    - type: main_score
+      value: 75.0830078125
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB HotpotQA (default)
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+      split: test
+      type: mteb/hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 38.143
+    - type: map_at_10
+      value: 55.916999999999994
+    - type: map_at_100
+      value: 56.706
+    - type: map_at_1000
+      value: 56.77100000000001
+    - type: map_at_20
+      value: 56.367
+    - type: map_at_3
+      value: 53.111
+    - type: map_at_5
+      value: 54.839000000000006
+    - type: mrr_at_1
+      value: 76.286
+    - type: mrr_at_10
+      value: 81.879
+    - type: mrr_at_100
+      value: 82.09100000000001
+    - type: mrr_at_1000
+      value: 82.101
+    - type: mrr_at_20
+      value: 82.01
+    - type: mrr_at_3
+      value: 80.972
+    - type: mrr_at_5
+      value: 81.537
+    - type: ndcg_at_1
+      value: 76.286
+    - type: ndcg_at_10
+      value: 64.673
+    - type: ndcg_at_100
+      value: 67.527
+    - type: ndcg_at_1000
+      value: 68.857
+    - type: ndcg_at_20
+      value: 65.822
+    - type: ndcg_at_3
+      value: 60.616
+    - type: ndcg_at_5
+      value: 62.827999999999996
+    - type: precision_at_1
+      value: 76.286
+    - type: precision_at_10
+      value: 13.196
+    - type: precision_at_100
+      value: 1.544
+    - type: precision_at_1000
+      value: 0.172
+    - type: precision_at_20
+      value: 6.968000000000001
+    - type: precision_at_3
+      value: 37.992
+    - type: precision_at_5
+      value: 24.54
+    - type: recall_at_1
+      value: 38.143
+    - type: recall_at_10
+      value: 65.982
+    - type: recall_at_100
+      value: 77.225
+    - type: recall_at_1000
+      value: 86.077
+    - type: recall_at_20
+      value: 69.68299999999999
+    - type: recall_at_3
+      value: 56.989000000000004
+    - type: recall_at_5
+      value: 61.35
+    - type: main_score
+      value: 64.673
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB IFlyTek (default)
+      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
+      split: validation
+      type: C-MTEB/IFlyTek-classification
+    metrics:
+    - type: accuracy
+      value: 41.67756829549827
+    - type: f1
+      value: 33.929325579581636
+    - type: f1_weighted
+      value: 43.03952025643197
+    - type: main_score
+      value: 41.67756829549827
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification (default)
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 91.90440000000001
+    - type: ap
+      value: 88.78663714603425
+    - type: ap_weighted
+      value: 88.78663714603425
+    - type: f1
+      value: 91.89564361975891
+    - type: f1_weighted
+      value: 91.89564361975891
+    - type: main_score
+      value: 91.90440000000001
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB InappropriatenessClassification (default)
+      revision: 601651fdc45ef243751676e62dd7a19f491c0285
+      split: test
+      type: ai-forever/inappropriateness-classification
+    metrics:
+    - type: accuracy
+      value: 61.0498046875
+    - type: ap
+      value: 57.04240566648215
+    - type: ap_weighted
+      value: 57.04240566648215
+    - type: f1
+      value: 60.867630038606954
+    - type: f1_weighted
+      value: 60.867630038606954
+    - type: main_score
+      value: 61.0498046875
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB JDReview (default)
+      revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
+      split: test
+      type: C-MTEB/JDReview-classification
+    metrics:
+    - type: accuracy
+      value: 83.50844277673546
+    - type: ap
+      value: 48.46732380712268
+    - type: ap_weighted
+      value: 48.46732380712268
+    - type: f1
+      value: 77.43967451387445
+    - type: f1_weighted
+      value: 84.78462929014114
+    - type: main_score
+      value: 83.50844277673546
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB KinopoiskClassification (default)
+      revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
+      split: test
+      type: ai-forever/kinopoisk-sentiment-classification
+    metrics:
+    - type: accuracy
+      value: 62.393333333333324
+    - type: f1
+      value: 61.35940129568015
+    - type: f1_weighted
+      value: 61.35940129568015
+    - type: main_score
+      value: 62.393333333333324
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB LCQMC (default)
+      revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
+      split: test
+      type: C-MTEB/LCQMC
+    metrics:
+    - type: cosine_pearson
+      value: 67.74375505907872
+    - type: cosine_spearman
+      value: 75.94582231399434
+    - type: euclidean_pearson
+      value: 74.52501692443582
+    - type: euclidean_spearman
+      value: 75.88428434746646
+    - type: main_score
+      value: 75.94582231399434
+    - type: manhattan_pearson
+      value: 74.55015441749529
+    - type: manhattan_spearman
+      value: 75.83288262176175
+    - type: pearson
+      value: 67.74375505907872
+    - type: spearman
+      value: 75.94582231399434
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB LEMBNarrativeQARetrieval (default)
+      revision: 6e346642246bfb4928c560ee08640dc84d074e8c
+      split: test
+      type: dwzhu/LongEmbed
+    metrics:
+    - type: map_at_1
+      value: 23.093
+    - type: map_at_10
+      value: 30.227999999999998
+    - type: map_at_100
+      value: 31.423000000000002
+    - type: map_at_1000
+      value: 31.533
+    - type: map_at_20
+      value: 30.835
+    - type: map_at_3
+      value: 27.983999999999998
+    - type: map_at_5
+      value: 29.253
+    - type: mrr_at_1
+      value: 23.093
+    - type: mrr_at_10
+      value: 30.227999999999998
+    - type: mrr_at_100
+      value: 31.423000000000002
+    - type: mrr_at_1000
+      value: 31.533
+    - type: mrr_at_20
+      value: 30.835
+    - type: mrr_at_3
+      value: 27.983999999999998
+    - type: mrr_at_5
+      value: 29.253
+    - type: ndcg_at_1
+      value: 23.093
+    - type: ndcg_at_10
+      value: 34.297
+    - type: ndcg_at_100
+      value: 41.049
+    - type: ndcg_at_1000
+      value: 43.566
+    - type: ndcg_at_20
+      value: 36.52
+    - type: ndcg_at_3
+      value: 29.629
+    - type: ndcg_at_5
+      value: 31.926
+    - type: precision_at_1
+      value: 23.093
+    - type: precision_at_10
+      value: 4.735
+    - type: precision_at_100
+      value: 0.8109999999999999
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_20
+      value: 2.8080000000000003
+    - type: precision_at_3
+      value: 11.468
+    - type: precision_at_5
+      value: 8.001
+    - type: recall_at_1
+      value: 23.093
+    - type: recall_at_10
+      value: 47.354
+    - type: recall_at_100
+      value: 81.147
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_20
+      value: 56.16799999999999
+    - type: recall_at_3
+      value: 34.405
+    - type: recall_at_5
+      value: 40.004
+    - type: main_score
+      value: 34.297
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB LEMBNeedleRetrieval (default)
+      revision: 6e346642246bfb4928c560ee08640dc84d074e8c
+      split: test_256
+      type: dwzhu/LongEmbed
+    metrics:
+    - type: map_at_1
+      value: 64.0
+    - type: map_at_10
+      value: 77.083
+    - type: map_at_100
+      value: 77.265
+    - type: map_at_1000
+      value: 77.265
+    - type: map_at_20
+      value: 77.265
+    - type: map_at_3
+      value: 76.333
+    - type: map_at_5
+      value: 76.833
+    - type: mrr_at_1
+      value: 64.0
+    - type: mrr_at_10
+      value: 77.083
+    - type: mrr_at_100
+      value: 77.265
+    - type: mrr_at_1000
+      value: 77.265
+    - type: mrr_at_20
+      value: 77.265
+    - type: mrr_at_3
+      value: 76.333
+    - type: mrr_at_5
+      value: 76.833
+    - type: ndcg_at_1
+      value: 64.0
+    - type: ndcg_at_10
+      value: 82.325
+    - type: ndcg_at_100
+      value: 82.883
+    - type: ndcg_at_1000
+      value: 82.883
+    - type: ndcg_at_20
+      value: 82.883
+    - type: ndcg_at_3
+      value: 80.833
+    - type: ndcg_at_5
+      value: 81.694
+    - type: precision_at_1
+      value: 64.0
+    - type: precision_at_10
+      value: 9.8
+    - type: precision_at_100
+      value: 1.0
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_20
+      value: 5.0
+    - type: precision_at_3
+      value: 31.333
+    - type: precision_at_5
+      value: 19.2
+    - type: recall_at_1
+      value: 64.0
+    - type: recall_at_10
+      value: 98.0
+    - type: recall_at_100
+      value: 100.0
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_20
+      value: 100.0
+    - type: recall_at_3
+      value: 94.0
+    - type: recall_at_5
+      value: 96.0
+    - type: main_score
+      value: 64.0
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB LEMBPasskeyRetrieval (default)
+      revision: 6e346642246bfb4928c560ee08640dc84d074e8c
+      split: test_256
+      type: dwzhu/LongEmbed
+    metrics:
+    - type: map_at_1
+      value: 100.0
+    - type: map_at_10
+      value: 100.0
+    - type: map_at_100
+      value: 100.0
+    - type: map_at_1000
+      value: 100.0
+    - type: map_at_20
+      value: 100.0
+    - type: map_at_3
+      value: 100.0
+    - type: map_at_5
+      value: 100.0
+    - type: mrr_at_1
+      value: 100.0
+    - type: mrr_at_10
+      value: 100.0
+    - type: mrr_at_100
+      value: 100.0
+    - type: mrr_at_1000
+      value: 100.0
+    - type: mrr_at_20
+      value: 100.0
+    - type: mrr_at_3
+      value: 100.0
+    - type: mrr_at_5
+      value: 100.0
+    - type: ndcg_at_1
+      value: 100.0
+    - type: ndcg_at_10
+      value: 100.0
+    - type: ndcg_at_100
+      value: 100.0
+    - type: ndcg_at_1000
+      value: 100.0
+    - type: ndcg_at_20
+      value: 100.0
+    - type: ndcg_at_3
+      value: 100.0
+    - type: ndcg_at_5
+      value: 100.0
+    - type: precision_at_1
+      value: 100.0
+    - type: precision_at_10
+      value: 10.0
+    - type: precision_at_100
+      value: 1.0
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_20
+      value: 5.0
+    - type: precision_at_3
+      value: 33.333
+    - type: precision_at_5
+      value: 20.0
+    - type: recall_at_1
+      value: 100.0
+    - type: recall_at_10
+      value: 100.0
+    - type: recall_at_100
+      value: 100.0
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_20
+      value: 100.0
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+      name: MTEB MSMARCO (default)
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+      split: dev
+      type: mteb/msmarco
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+    - type: recall_at_5
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+    - type: main_score
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+    task:
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+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
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+    - type: f1_weighted
+      value: 97.49331776181019
+    - type: main_score
+      value: 97.4874601003192
+    task:
+      type: Classification
+  - dataset:
+      config: de
+      name: MTEB MTOPDomainClassification (de)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
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+    - type: main_score
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+    task:
+      type: Classification
+  - dataset:
+      config: es
+      name: MTEB MTOPDomainClassification (es)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 97.17144763175452
+    - type: f1
+      value: 96.81785681878274
+    - type: f1_weighted
+      value: 97.1778974586874
+    - type: main_score
+      value: 97.17144763175452
+    task:
+      type: Classification
+  - dataset:
+      config: fr
+      name: MTEB MTOPDomainClassification (fr)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 96.30128405887879
+    - type: f1
+      value: 95.94555923088487
+    - type: f1_weighted
+      value: 96.30399416794926
+    - type: main_score
+      value: 96.30128405887879
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 84.53488372093022
+    - type: f1
+      value: 61.77995074251401
+    - type: f1_weighted
+      value: 86.8005170485101
+    - type: main_score
+      value: 84.53488372093022
+    task:
+      type: Classification
+  - dataset:
+      config: de
+      name: MTEB MTOPIntentClassification (de)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 80.79459002535924
+    - type: f1
+      value: 56.08938302001448
+    - type: f1_weighted
+      value: 83.66582131948252
+    - type: main_score
+      value: 80.79459002535924
+    task:
+      type: Classification
+  - dataset:
+      config: es
+      name: MTEB MTOPIntentClassification (es)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 84.7765176784523
+    - type: f1
+      value: 61.39860057885528
+    - type: f1_weighted
+      value: 86.94881745670745
+    - type: main_score
+      value: 84.7765176784523
+    task:
+      type: Classification
+  - dataset:
+      config: fr
+      name: MTEB MTOPIntentClassification (fr)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 82.2079549013467
+    - type: f1
+      value: 59.90260478749016
+    - type: f1_weighted
+      value: 84.36861708593257
+    - type: main_score
+      value: 82.2079549013467
+    task:
+      type: Classification
+  - dataset:
+      config: eng
+      name: MTEB MasakhaNEWSClassification (eng)
+      revision: 18193f187b92da67168c655c9973a165ed9593dd
+      split: test
+      type: mteb/masakhanews
+    metrics:
+    - type: accuracy
+      value: 74.98945147679325
+    - type: f1
+      value: 74.3157483560261
+    - type: f1_weighted
+      value: 75.01179008904884
+    - type: main_score
+      value: 74.98945147679325
+    task:
+      type: Classification
+  - dataset:
+      config: fra
+      name: MTEB MasakhaNEWSClassification (fra)
+      revision: 18193f187b92da67168c655c9973a165ed9593dd
+      split: test
+      type: mteb/masakhanews
+    metrics:
+    - type: accuracy
+      value: 74.02843601895735
+    - type: f1
+      value: 70.40326349620732
+    - type: f1_weighted
+      value: 74.6596277063484
+    - type: main_score
+      value: 74.02843601895735
+    task:
+      type: Classification
+  - dataset:
+      config: amh
+      name: MTEB MasakhaNEWSClusteringP2P (amh)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 69.45780291725053
+    - type: v_measure
+      value: 69.45780291725053
+    - type: v_measure_std
+      value: 36.54340055904091
+    task:
+      type: Clustering
+  - dataset:
+      config: eng
+      name: MTEB MasakhaNEWSClusteringP2P (eng)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 64.88996119332239
+    - type: v_measure
+      value: 64.88996119332239
+    - type: v_measure_std
+      value: 30.017223408197268
+    task:
+      type: Clustering
+  - dataset:
+      config: fra
+      name: MTEB MasakhaNEWSClusteringP2P (fra)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 42.362383958691666
+    - type: v_measure
+      value: 42.362383958691666
+    - type: v_measure_std
+      value: 37.61076788039063
+    task:
+      type: Clustering
+  - dataset:
+      config: hau
+      name: MTEB MasakhaNEWSClusteringP2P (hau)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 43.29201252405562
+    - type: v_measure
+      value: 43.29201252405562
+    - type: v_measure_std
+      value: 34.31987945146255
+    task:
+      type: Clustering
+  - dataset:
+      config: ibo
+      name: MTEB MasakhaNEWSClusteringP2P (ibo)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 33.59926542995238
+    - type: v_measure
+      value: 33.59926542995238
+    - type: v_measure_std
+      value: 35.70048601084112
+    task:
+      type: Clustering
+  - dataset:
+      config: lin
+      name: MTEB MasakhaNEWSClusteringP2P (lin)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 67.58487601893106
+    - type: v_measure
+      value: 67.58487601893106
+    - type: v_measure_std
+      value: 35.16784970777931
+    task:
+      type: Clustering
+  - dataset:
+      config: lug
+      name: MTEB MasakhaNEWSClusteringP2P (lug)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 50.01220872023533
+    - type: v_measure
+      value: 50.01220872023533
+    - type: v_measure_std
+      value: 41.87411574676182
+    task:
+      type: Clustering
+  - dataset:
+      config: orm
+      name: MTEB MasakhaNEWSClusteringP2P (orm)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 29.007847502598317
+    - type: v_measure
+      value: 29.007847502598317
+    - type: v_measure_std
+      value: 38.374997395079994
+    task:
+      type: Clustering
+  - dataset:
+      config: pcm
+      name: MTEB MasakhaNEWSClusteringP2P (pcm)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 79.13520228554611
+    - type: v_measure
+      value: 79.13520228554611
+    - type: v_measure_std
+      value: 18.501843848275183
+    task:
+      type: Clustering
+  - dataset:
+      config: run
+      name: MTEB MasakhaNEWSClusteringP2P (run)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 60.317213909746656
+    - type: v_measure
+      value: 60.317213909746656
+    - type: v_measure_std
+      value: 36.500281823747386
+    task:
+      type: Clustering
+  - dataset:
+      config: sna
+      name: MTEB MasakhaNEWSClusteringP2P (sna)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 59.395277358240946
+    - type: v_measure
+      value: 59.395277358240946
+    - type: v_measure_std
+      value: 37.500916816164654
+    task:
+      type: Clustering
+  - dataset:
+      config: som
+      name: MTEB MasakhaNEWSClusteringP2P (som)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 38.18638688704302
+    - type: v_measure
+      value: 38.18638688704302
+    - type: v_measure_std
+      value: 35.453681137564466
+    task:
+      type: Clustering
+  - dataset:
+      config: swa
+      name: MTEB MasakhaNEWSClusteringP2P (swa)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 29.49230755729658
+    - type: v_measure
+      value: 29.49230755729658
+    - type: v_measure_std
+      value: 28.284313285264645
+    task:
+      type: Clustering
+  - dataset:
+      config: tir
+      name: MTEB MasakhaNEWSClusteringP2P (tir)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 60.632258622750115
+    - type: v_measure
+      value: 60.632258622750115
+    - type: v_measure_std
+      value: 34.429711214740564
+    task:
+      type: Clustering
+  - dataset:
+      config: xho
+      name: MTEB MasakhaNEWSClusteringP2P (xho)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 41.76322918806381
+    - type: v_measure
+      value: 41.76322918806381
+    - type: v_measure_std
+      value: 36.43245296200775
+    task:
+      type: Clustering
+  - dataset:
+      config: yor
+      name: MTEB MasakhaNEWSClusteringP2P (yor)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 33.17083910808645
+    - type: v_measure
+      value: 33.17083910808645
+    - type: v_measure_std
+      value: 34.87547994284835
+    task:
+      type: Clustering
+  - dataset:
+      config: amh
+      name: MTEB MasakhaNEWSClusteringS2S (amh)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 60.95132147787602
+    - type: v_measure
+      value: 60.95132147787602
+    - type: v_measure_std
+      value: 37.330148394033365
+    task:
+      type: Clustering
+  - dataset:
+      config: eng
+      name: MTEB MasakhaNEWSClusteringS2S (eng)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 60.974810831426595
+    - type: v_measure
+      value: 60.974810831426595
+    - type: v_measure_std
+      value: 24.934675467507827
+    task:
+      type: Clustering
+  - dataset:
+      config: fra
+      name: MTEB MasakhaNEWSClusteringS2S (fra)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 44.479206673553335
+    - type: v_measure
+      value: 44.479206673553335
+    - type: v_measure_std
+      value: 32.58254804499339
+    task:
+      type: Clustering
+  - dataset:
+      config: hau
+      name: MTEB MasakhaNEWSClusteringS2S (hau)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 26.4742082741682
+    - type: v_measure
+      value: 26.4742082741682
+    - type: v_measure_std
+      value: 22.344929192323097
+    task:
+      type: Clustering
+  - dataset:
+      config: ibo
+      name: MTEB MasakhaNEWSClusteringS2S (ibo)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 38.906129911741985
+    - type: v_measure
+      value: 38.906129911741985
+    - type: v_measure_std
+      value: 34.785601792668444
+    task:
+      type: Clustering
+  - dataset:
+      config: lin
+      name: MTEB MasakhaNEWSClusteringS2S (lin)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 62.60982020876592
+    - type: v_measure
+      value: 62.60982020876592
+    - type: v_measure_std
+      value: 40.7368955715045
+    task:
+      type: Clustering
+  - dataset:
+      config: lug
+      name: MTEB MasakhaNEWSClusteringS2S (lug)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 42.70424106365967
+    - type: v_measure
+      value: 42.70424106365967
+    - type: v_measure_std
+      value: 46.80946241135087
+    task:
+      type: Clustering
+  - dataset:
+      config: orm
+      name: MTEB MasakhaNEWSClusteringS2S (orm)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 28.609942199922322
+    - type: v_measure
+      value: 28.609942199922322
+    - type: v_measure_std
+      value: 38.46685040191088
+    task:
+      type: Clustering
+  - dataset:
+      config: pcm
+      name: MTEB MasakhaNEWSClusteringS2S (pcm)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 76.83901348810822
+    - type: v_measure
+      value: 76.83901348810822
+    - type: v_measure_std
+      value: 17.57617141269189
+    task:
+      type: Clustering
+  - dataset:
+      config: run
+      name: MTEB MasakhaNEWSClusteringS2S (run)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 46.89757547846193
+    - type: v_measure
+      value: 46.89757547846193
+    - type: v_measure_std
+      value: 44.58903590203438
+    task:
+      type: Clustering
+  - dataset:
+      config: sna
+      name: MTEB MasakhaNEWSClusteringS2S (sna)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 55.37185207068829
+    - type: v_measure
+      value: 55.37185207068829
+    - type: v_measure_std
+      value: 36.944574863543004
+    task:
+      type: Clustering
+  - dataset:
+      config: som
+      name: MTEB MasakhaNEWSClusteringS2S (som)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 37.44211021681754
+    - type: v_measure
+      value: 37.44211021681754
+    - type: v_measure_std
+      value: 33.41469994463241
+    task:
+      type: Clustering
+  - dataset:
+      config: swa
+      name: MTEB MasakhaNEWSClusteringS2S (swa)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 26.020680621216062
+    - type: v_measure
+      value: 26.020680621216062
+    - type: v_measure_std
+      value: 25.480037522570413
+    task:
+      type: Clustering
+  - dataset:
+      config: tir
+      name: MTEB MasakhaNEWSClusteringS2S (tir)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 63.74306846771303
+    - type: v_measure
+      value: 63.74306846771303
+    - type: v_measure_std
+      value: 32.19119631078685
+    task:
+      type: Clustering
+  - dataset:
+      config: xho
+      name: MTEB MasakhaNEWSClusteringS2S (xho)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 24.580890519243777
+    - type: v_measure
+      value: 24.580890519243777
+    - type: v_measure_std
+      value: 37.941836363967106
+    task:
+      type: Clustering
+  - dataset:
+      config: yor
+      name: MTEB MasakhaNEWSClusteringS2S (yor)
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+      split: test
+      type: masakhane/masakhanews
+    metrics:
+    - type: main_score
+      value: 43.63458888828314
+    - type: v_measure
+      value: 43.63458888828314
+    - type: v_measure_std
+      value: 31.28169350649098
+    task:
+      type: Clustering
+  - dataset:
+      config: pl
+      name: MTEB MassiveIntentClassification (pl)
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 75.37323470073974
+    - type: f1
+      value: 71.1836877753734
+    - type: f1_weighted
+      value: 75.72073213955457
+    - type: main_score
+      value: 75.37323470073974
+    task:
+      type: Classification
+  - dataset:
+      config: de
+      name: MTEB MassiveIntentClassification (de)
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 74.83523873570948
+    - type: f1
+      value: 70.72375821116886
+    - type: f1_weighted
+      value: 75.20800490010755
+    - type: main_score
+      value: 74.83523873570948
+    task:
+      type: Classification
+  - dataset:
+      config: es
+      name: MTEB MassiveIntentClassification (es)
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 75.31607262945528
+    - type: f1
+      value: 72.06063554897662
+    - type: f1_weighted
+      value: 75.72438161355252
+    - type: main_score
+      value: 75.31607262945528
+    task:
+      type: Classification
+  - dataset:
+      config: ru
+      name: MTEB MassiveIntentClassification (ru)
+      revision: 4672e20407010da34463acc759c162ca9734bca6
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
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+      name: MTEB MultilingualSentiment (default)
+      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
+      split: test
+      type: C-MTEB/MultilingualSentiment-classification
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+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB NFCorpus (default)
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+      split: test
+      type: mteb/nfcorpus
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+    - type: map_at_5
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+    - type: mrr_at_5
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+    - type: ndcg_at_1
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+    - type: ndcg_at_100
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+    - type: ndcg_at_1000
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+    - type: recall_at_3
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+    - type: recall_at_5
+      value: 13.406
+    - type: main_score
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+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ (default)
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+      split: test
+      type: mteb/nq
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+      value: 57.179
+    - type: map_at_100
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+    - type: mrr_at_1000
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+    - type: mrr_at_20
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+    - type: mrr_at_3
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+    - type: mrr_at_5
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+    - type: ndcg_at_1
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+    - type: ndcg_at_10
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+    - type: ndcg_at_100
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+    - type: ndcg_at_1000
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+    - type: precision_at_5
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+    - type: recall_at_1
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+    - type: recall_at_10
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+    - type: recall_at_100
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+    - type: recall_at_20
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+    - type: recall_at_3
+      value: 65.73
+    - type: recall_at_5
+      value: 73.894
+    - type: main_score
+      value: 64.23100000000001
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Ocnli (default)
+      revision: 66e76a618a34d6d565d5538088562851e6daa7ec
+      split: validation
+      type: C-MTEB/OCNLI
+    metrics:
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+    - type: cosine_accuracy_threshold
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+    - type: cosine_ap
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+    - type: euclidean_accuracy
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+    - type: euclidean_accuracy_threshold
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+    - type: euclidean_ap
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+    - type: euclidean_precision
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+    - type: manhattan_recall
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+    - type: max_accuracy
+      value: 63.02111532214402
+    - type: max_ap
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+      value: 69.08382066276802
+    - type: max_precision
+      value: 57.485875706214685
+    - type: max_recall
+      value: 94.82576557550159
+    - type: similarity_accuracy
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+    - type: similarity_accuracy_threshold
+      value: 79.70921993255615
+    - type: similarity_ap
+      value: 66.41380155495659
+    - type: similarity_f1
+      value: 68.89547185780786
+    - type: similarity_f1_threshold
+      value: 72.91591167449951
+    - type: similarity_precision
+      value: 57.485875706214685
+    - type: similarity_recall
+      value: 85.95564941921859
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB OnlineShopping (default)
+      revision: e610f2ebd179a8fda30ae534c3878750a96db120
+      split: test
+      type: C-MTEB/OnlineShopping-classification
+    metrics:
+    - type: accuracy
+      value: 91.88000000000001
+    - type: ap
+      value: 89.52463684448476
+    - type: ap_weighted
+      value: 89.52463684448476
+    - type: f1
+      value: 91.86313022306673
+    - type: f1_weighted
+      value: 91.87806318146912
+    - type: main_score
+      value: 91.88000000000001
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB OpusparcusPC (en)
+      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
+      split: test.full
+      type: GEM/opusparcus
+    metrics:
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+      value: 92.65578635014838
+    - type: cosine_accuracy_threshold
+      value: 74.02530312538147
+    - type: cosine_ap
+      value: 98.3834226153613
+    - type: cosine_f1
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+    - type: cosine_precision
+      value: 95.562435500516
+    - type: cosine_recall
+      value: 94.29735234215886
+    - type: dot_accuracy
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+    - type: dot_accuracy_threshold
+      value: 34452.29187011719
+    - type: dot_ap
+      value: 98.1237257754439
+    - type: dot_f1
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+    - type: dot_f1_threshold
+      value: 33670.41931152344
+    - type: dot_precision
+      value: 92.9633300297324
+    - type: dot_recall
+      value: 95.5193482688391
+    - type: euclidean_accuracy
+      value: 92.28486646884274
+    - type: euclidean_accuracy_threshold
+      value: 1602.8022766113281
+    - type: euclidean_ap
+      value: 98.3099021504706
+    - type: euclidean_f1
+      value: 94.75277497477296
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+      value: 1604.7462463378906
+    - type: euclidean_precision
+      value: 93.89999999999999
+    - type: euclidean_recall
+      value: 95.62118126272912
+    - type: main_score
+      value: 98.3834226153613
+    - type: manhattan_accuracy
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+    - type: manhattan_accuracy_threshold
+      value: 38872.90954589844
+    - type: manhattan_ap
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+      value: 38872.90954589844
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+      value: 94.24823410696267
+    - type: manhattan_recall
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+    - type: max_accuracy
+      value: 92.65578635014838
+    - type: max_ap
+      value: 98.3834226153613
+    - type: max_f1
+      value: 94.92567913890312
+    - type: max_precision
+      value: 95.562435500516
+    - type: max_recall
+      value: 95.62118126272912
+    - type: similarity_accuracy
+      value: 92.65578635014838
+    - type: similarity_accuracy_threshold
+      value: 74.02530312538147
+    - type: similarity_ap
+      value: 98.3834226153613
+    - type: similarity_f1
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+      value: 74.02530312538147
+    - type: similarity_precision
+      value: 95.562435500516
+    - type: similarity_recall
+      value: 94.29735234215886
+    task:
+      type: PairClassification
+  - dataset:
+      config: de
+      name: MTEB OpusparcusPC (de)
+      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
+      split: test.full
+      type: GEM/opusparcus
+    metrics:
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+      value: 87.72178850248403
+    - type: cosine_accuracy_threshold
+      value: 73.33863377571106
+    - type: cosine_ap
+      value: 96.98901408834976
+    - type: cosine_f1
+      value: 91.89944134078212
+    - type: cosine_f1_threshold
+      value: 71.45810127258301
+    - type: cosine_precision
+      value: 89.64577656675749
+    - type: cosine_recall
+      value: 94.26934097421203
+    - type: dot_accuracy
+      value: 86.30234208658624
+    - type: dot_accuracy_threshold
+      value: 32027.130126953125
+    - type: dot_ap
+      value: 96.12260574893256
+    - type: dot_f1
+      value: 91.31602506714414
+    - type: dot_f1_threshold
+      value: 30804.376220703125
+    - type: dot_precision
+      value: 85.93091828138164
+    - type: dot_recall
+      value: 97.42120343839542
+    - type: euclidean_accuracy
+      value: 87.9347054648687
+    - type: euclidean_accuracy_threshold
+      value: 1609.6670150756836
+    - type: euclidean_ap
+      value: 97.00238860358252
+    - type: euclidean_f1
+      value: 92.1089063221043
+    - type: euclidean_f1_threshold
+      value: 1641.8487548828125
+    - type: euclidean_precision
+      value: 89.10714285714286
+    - type: euclidean_recall
+      value: 95.31996179560649
+    - type: main_score
+      value: 97.00238860358252
+    - type: manhattan_accuracy
+      value: 87.72178850248403
+    - type: manhattan_accuracy_threshold
+      value: 40137.060546875
+    - type: manhattan_ap
+      value: 96.98653728159941
+    - type: manhattan_f1
+      value: 92.03865623561896
+    - type: manhattan_f1_threshold
+      value: 40137.060546875
+    - type: manhattan_precision
+      value: 88.80994671403198
+    - type: manhattan_recall
+      value: 95.51098376313276
+    - type: max_accuracy
+      value: 87.9347054648687
+    - type: max_ap
+      value: 97.00238860358252
+    - type: max_f1
+      value: 92.1089063221043
+    - type: max_precision
+      value: 89.64577656675749
+    - type: max_recall
+      value: 97.42120343839542
+    - type: similarity_accuracy
+      value: 87.72178850248403
+    - type: similarity_accuracy_threshold
+      value: 73.33863377571106
+    - type: similarity_ap
+      value: 96.98901408834976
+    - type: similarity_f1
+      value: 91.89944134078212
+    - type: similarity_f1_threshold
+      value: 71.45810127258301
+    - type: similarity_precision
+      value: 89.64577656675749
+    - type: similarity_recall
+      value: 94.26934097421203
+    task:
+      type: PairClassification
+  - dataset:
+      config: fr
+      name: MTEB OpusparcusPC (fr)
+      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
+      split: test.full
+      type: GEM/opusparcus
+    metrics:
+    - type: cosine_accuracy
+      value: 80.92643051771117
+    - type: cosine_accuracy_threshold
+      value: 76.68856382369995
+    - type: cosine_ap
+      value: 93.74622381534307
+    - type: cosine_f1
+      value: 87.12328767123287
+    - type: cosine_f1_threshold
+      value: 71.64022922515869
+    - type: cosine_precision
+      value: 80.64243448858834
+    - type: cosine_recall
+      value: 94.73684210526315
+    - type: dot_accuracy
+      value: 80.858310626703
+    - type: dot_accuracy_threshold
+      value: 34028.3935546875
+    - type: dot_ap
+      value: 91.18448457633308
+    - type: dot_f1
+      value: 86.82606657290202
+    - type: dot_f1_threshold
+      value: 34028.3935546875
+    - type: dot_precision
+      value: 82.2380106571936
+    - type: dot_recall
+      value: 91.9563058589871
+    - type: euclidean_accuracy
+      value: 80.858310626703
+    - type: euclidean_accuracy_threshold
+      value: 1595.7651138305664
+    - type: euclidean_ap
+      value: 93.8182717829648
+    - type: euclidean_f1
+      value: 87.04044117647058
+    - type: euclidean_f1_threshold
+      value: 1609.2475891113281
+    - type: euclidean_precision
+      value: 81.00940975192472
+    - type: euclidean_recall
+      value: 94.04170804369414
+    - type: main_score
+      value: 93.8182717829648
+    - type: manhattan_accuracy
+      value: 80.99455040871935
+    - type: manhattan_accuracy_threshold
+      value: 38092.132568359375
+    - type: manhattan_ap
+      value: 93.77563401151711
+    - type: manhattan_f1
+      value: 86.91983122362869
+    - type: manhattan_f1_threshold
+      value: 38092.132568359375
+    - type: manhattan_precision
+      value: 82.32682060390763
+    - type: manhattan_recall
+      value: 92.05561072492551
+    - type: max_accuracy
+      value: 80.99455040871935
+    - type: max_ap
+      value: 93.8182717829648
+    - type: max_f1
+      value: 87.12328767123287
+    - type: max_precision
+      value: 82.32682060390763
+    - type: max_recall
+      value: 94.73684210526315
+    - type: similarity_accuracy
+      value: 80.92643051771117
+    - type: similarity_accuracy_threshold
+      value: 76.68856382369995
+    - type: similarity_ap
+      value: 93.74622381534307
+    - type: similarity_f1
+      value: 87.12328767123287
+    - type: similarity_f1_threshold
+      value: 71.64022922515869
+    - type: similarity_precision
+      value: 80.64243448858834
+    - type: similarity_recall
+      value: 94.73684210526315
+    task:
+      type: PairClassification
+  - dataset:
+      config: ru
+      name: MTEB OpusparcusPC (ru)
+      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
+      split: test.full
+      type: GEM/opusparcus
+    metrics:
+    - type: cosine_accuracy
+      value: 76.83823529411765
+    - type: cosine_accuracy_threshold
+      value: 72.70769476890564
+    - type: cosine_ap
+      value: 89.56692049908222
+    - type: cosine_f1
+      value: 83.99832003359934
+    - type: cosine_f1_threshold
+      value: 70.9052324295044
+    - type: cosine_precision
+      value: 76.16146230007617
+    - type: cosine_recall
+      value: 93.63295880149812
+    - type: dot_accuracy
+      value: 76.28676470588235
+    - type: dot_accuracy_threshold
+      value: 33740.68908691406
+    - type: dot_ap
+      value: 87.77185177141567
+    - type: dot_f1
+      value: 83.62251375370292
+    - type: dot_f1_threshold
+      value: 32726.611328125
+    - type: dot_precision
+      value: 76.29343629343629
+    - type: dot_recall
+      value: 92.50936329588015
+    - type: euclidean_accuracy
+      value: 77.32843137254902
+    - type: euclidean_accuracy_threshold
+      value: 1566.510009765625
+    - type: euclidean_ap
+      value: 89.60605626791111
+    - type: euclidean_f1
+      value: 84.06546080964686
+    - type: euclidean_f1_threshold
+      value: 1576.4202117919922
+    - type: euclidean_precision
+      value: 77.83094098883574
+    - type: euclidean_recall
+      value: 91.38576779026218
+    - type: main_score
+      value: 89.60605626791111
+    - type: manhattan_accuracy
+      value: 76.89950980392157
+    - type: manhattan_accuracy_threshold
+      value: 38202.215576171875
+    - type: manhattan_ap
+      value: 89.55766894104868
+    - type: manhattan_f1
+      value: 83.80462724935732
+    - type: manhattan_f1_threshold
+      value: 38934.375
+    - type: manhattan_precision
+      value: 77.25118483412322
+    - type: manhattan_recall
+      value: 91.57303370786516
+    - type: max_accuracy
+      value: 77.32843137254902
+    - type: max_ap
+      value: 89.60605626791111
+    - type: max_f1
+      value: 84.06546080964686
+    - type: max_precision
+      value: 77.83094098883574
+    - type: max_recall
+      value: 93.63295880149812
+    - type: similarity_accuracy
+      value: 76.83823529411765
+    - type: similarity_accuracy_threshold
+      value: 72.70769476890564
+    - type: similarity_ap
+      value: 89.56692049908222
+    - type: similarity_f1
+      value: 83.99832003359934
+    - type: similarity_f1_threshold
+      value: 70.9052324295044
+    - type: similarity_precision
+      value: 76.16146230007617
+    - type: similarity_recall
+      value: 93.63295880149812
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB PAC (default)
+      revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
+      split: test
+      type: laugustyniak/abusive-clauses-pl
+    metrics:
+    - type: accuracy
+      value: 68.39559803069794
+    - type: ap
+      value: 77.68074206719457
+    - type: ap_weighted
+      value: 77.68074206719457
+    - type: f1
+      value: 66.23485605467732
+    - type: f1_weighted
+      value: 69.03201442129347
+    - type: main_score
+      value: 68.39559803069794
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB PAWSX (default)
+      revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
+      split: test
+      type: C-MTEB/PAWSX
+    metrics:
+    - type: cosine_pearson
+      value: 13.161523266433587
+    - type: cosine_spearman
+      value: 15.557333873773386
+    - type: euclidean_pearson
+      value: 17.147508431907525
+    - type: euclidean_spearman
+      value: 15.664112857732146
+    - type: main_score
+      value: 15.557333873773386
+    - type: manhattan_pearson
+      value: 17.130875906264386
+    - type: manhattan_spearman
+      value: 15.624397342229637
+    - type: pearson
+      value: 13.161523266433587
+    - type: spearman
+      value: 15.557333873773386
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB PSC (default)
+      revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
+      split: test
+      type: PL-MTEB/psc-pairclassification
+    metrics:
+    - type: cosine_accuracy
+      value: 97.86641929499072
+    - type: cosine_accuracy_threshold
+      value: 79.0391206741333
+    - type: cosine_ap
+      value: 99.19403807771533
+    - type: cosine_f1
+      value: 96.45608628659475
+    - type: cosine_f1_threshold
+      value: 79.0391206741333
+    - type: cosine_precision
+      value: 97.50778816199377
+    - type: cosine_recall
+      value: 95.42682926829268
+    - type: dot_accuracy
+      value: 98.14471243042672
+    - type: dot_accuracy_threshold
+      value: 29808.1787109375
+    - type: dot_ap
+      value: 99.331999859971
+    - type: dot_f1
+      value: 97.01492537313433
+    - type: dot_f1_threshold
+      value: 29808.1787109375
+    - type: dot_precision
+      value: 95.02923976608187
+    - type: dot_recall
+      value: 99.08536585365853
+    - type: euclidean_accuracy
+      value: 97.49536178107606
+    - type: euclidean_accuracy_threshold
+      value: 1276.227855682373
+    - type: euclidean_ap
+      value: 98.91056467717377
+    - type: euclidean_f1
+      value: 95.83975346687212
+    - type: euclidean_f1_threshold
+      value: 1276.227855682373
+    - type: euclidean_precision
+      value: 96.88473520249221
+    - type: euclidean_recall
+      value: 94.8170731707317
+    - type: main_score
+      value: 99.331999859971
+    - type: manhattan_accuracy
+      value: 97.49536178107606
+    - type: manhattan_accuracy_threshold
+      value: 31097.674560546875
+    - type: manhattan_ap
+      value: 98.95694691792707
+    - type: manhattan_f1
+      value: 95.83975346687212
+    - type: manhattan_f1_threshold
+      value: 31097.674560546875
+    - type: manhattan_precision
+      value: 96.88473520249221
+    - type: manhattan_recall
+      value: 94.8170731707317
+    - type: max_accuracy
+      value: 98.14471243042672
+    - type: max_ap
+      value: 99.331999859971
+    - type: max_f1
+      value: 97.01492537313433
+    - type: max_precision
+      value: 97.50778816199377
+    - type: max_recall
+      value: 99.08536585365853
+    - type: similarity_accuracy
+      value: 97.86641929499072
+    - type: similarity_accuracy_threshold
+      value: 79.0391206741333
+    - type: similarity_ap
+      value: 99.19403807771533
+    - type: similarity_f1
+      value: 96.45608628659475
+    - type: similarity_f1_threshold
+      value: 79.0391206741333
+    - type: similarity_precision
+      value: 97.50778816199377
+    - type: similarity_recall
+      value: 95.42682926829268
+    task:
+      type: PairClassification
+  - dataset:
+      config: en
+      name: MTEB PawsXPairClassification (en)
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+      split: test
+      type: google-research-datasets/paws-x
+    metrics:
+    - type: cosine_accuracy
+      value: 61.8
+    - type: cosine_accuracy_threshold
+      value: 99.5664119720459
+    - type: cosine_ap
+      value: 60.679317786040585
+    - type: cosine_f1
+      value: 63.17354143441101
+    - type: cosine_f1_threshold
+      value: 97.22164869308472
+    - type: cosine_precision
+      value: 47.6457399103139
+    - type: cosine_recall
+      value: 93.71554575523705
+    - type: dot_accuracy
+      value: 55.7
+    - type: dot_accuracy_threshold
+      value: 48353.62548828125
+    - type: dot_ap
+      value: 48.53805970536875
+    - type: dot_f1
+      value: 62.42214532871972
+    - type: dot_f1_threshold
+      value: 38215.53955078125
+    - type: dot_precision
+      value: 45.48663640948058
+    - type: dot_recall
+      value: 99.44873208379272
+    - type: euclidean_accuracy
+      value: 61.75000000000001
+    - type: euclidean_accuracy_threshold
+      value: 189.0761137008667
+    - type: euclidean_ap
+      value: 60.55517418691518
+    - type: euclidean_f1
+      value: 63.07977736549165
+    - type: euclidean_f1_threshold
+      value: 504.3168067932129
+    - type: euclidean_precision
+      value: 47.53914988814318
+    - type: euclidean_recall
+      value: 93.71554575523705
+    - type: main_score
+      value: 60.679317786040585
+    - type: manhattan_accuracy
+      value: 61.9
+    - type: manhattan_accuracy_threshold
+      value: 4695.778274536133
+    - type: manhattan_ap
+      value: 60.48686620413608
+    - type: manhattan_f1
+      value: 62.92880855772778
+    - type: manhattan_f1_threshold
+      value: 12542.36831665039
+    - type: manhattan_precision
+      value: 47.28381374722838
+    - type: manhattan_recall
+      value: 94.04630650496141
+    - type: max_accuracy
+      value: 61.9
+    - type: max_ap
+      value: 60.679317786040585
+    - type: max_f1
+      value: 63.17354143441101
+    - type: max_precision
+      value: 47.6457399103139
+    - type: max_recall
+      value: 99.44873208379272
+    - type: similarity_accuracy
+      value: 61.8
+    - type: similarity_accuracy_threshold
+      value: 99.5664119720459
+    - type: similarity_ap
+      value: 60.679317786040585
+    - type: similarity_f1
+      value: 63.17354143441101
+    - type: similarity_f1_threshold
+      value: 97.22164869308472
+    - type: similarity_precision
+      value: 47.6457399103139
+    - type: similarity_recall
+      value: 93.71554575523705
+    task:
+      type: PairClassification
+  - dataset:
+      config: de
+      name: MTEB PawsXPairClassification (de)
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+      split: test
+      type: google-research-datasets/paws-x
+    metrics:
+    - type: cosine_accuracy
+      value: 60.25
+    - type: cosine_accuracy_threshold
+      value: 99.54338073730469
+    - type: cosine_ap
+      value: 56.7863613689054
+    - type: cosine_f1
+      value: 62.23499820337766
+    - type: cosine_f1_threshold
+      value: 89.95014429092407
+    - type: cosine_precision
+      value: 45.86864406779661
+    - type: cosine_recall
+      value: 96.75977653631284
+    - type: dot_accuracy
+      value: 56.8
+    - type: dot_accuracy_threshold
+      value: 47349.78332519531
+    - type: dot_ap
+      value: 49.7857806061729
+    - type: dot_f1
+      value: 62.31225986727209
+    - type: dot_f1_threshold
+      value: 30143.206787109375
+    - type: dot_precision
+      value: 45.32520325203252
+    - type: dot_recall
+      value: 99.66480446927373
+    - type: euclidean_accuracy
+      value: 60.3
+    - type: euclidean_accuracy_threshold
+      value: 219.78106498718262
+    - type: euclidean_ap
+      value: 56.731544327179606
+    - type: euclidean_f1
+      value: 62.19895287958115
+    - type: euclidean_f1_threshold
+      value: 1792.1623229980469
+    - type: euclidean_precision
+      value: 45.22842639593909
+    - type: euclidean_recall
+      value: 99.55307262569832
+    - type: main_score
+      value: 56.7863613689054
+    - type: manhattan_accuracy
+      value: 60.150000000000006
+    - type: manhattan_accuracy_threshold
+      value: 5104.503631591797
+    - type: manhattan_ap
+      value: 56.70304479768734
+    - type: manhattan_f1
+      value: 62.22067039106145
+    - type: manhattan_f1_threshold
+      value: 42839.471435546875
+    - type: manhattan_precision
+      value: 45.2513966480447
+    - type: manhattan_recall
+      value: 99.55307262569832
+    - type: max_accuracy
+      value: 60.3
+    - type: max_ap
+      value: 56.7863613689054
+    - type: max_f1
+      value: 62.31225986727209
+    - type: max_precision
+      value: 45.86864406779661
+    - type: max_recall
+      value: 99.66480446927373
+    - type: similarity_accuracy
+      value: 60.25
+    - type: similarity_accuracy_threshold
+      value: 99.54338073730469
+    - type: similarity_ap
+      value: 56.7863613689054
+    - type: similarity_f1
+      value: 62.23499820337766
+    - type: similarity_f1_threshold
+      value: 89.95014429092407
+    - type: similarity_precision
+      value: 45.86864406779661
+    - type: similarity_recall
+      value: 96.75977653631284
+    task:
+      type: PairClassification
+  - dataset:
+      config: es
+      name: MTEB PawsXPairClassification (es)
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+      split: test
+      type: google-research-datasets/paws-x
+    metrics:
+    - type: cosine_accuracy
+      value: 59.699999999999996
+    - type: cosine_accuracy_threshold
+      value: 99.55930709838867
+    - type: cosine_ap
+      value: 57.31662248806265
+    - type: cosine_f1
+      value: 62.444061962134256
+    - type: cosine_f1_threshold
+      value: 74.75898265838623
+    - type: cosine_precision
+      value: 45.3953953953954
+    - type: cosine_recall
+      value: 100.0
+    - type: dot_accuracy
+      value: 55.900000000000006
+    - type: dot_accuracy_threshold
+      value: 47512.90283203125
+    - type: dot_ap
+      value: 49.39339147787568
+    - type: dot_f1
+      value: 62.487082328625554
+    - type: dot_f1_threshold
+      value: 34989.03503417969
+    - type: dot_precision
+      value: 45.44088176352705
+    - type: dot_recall
+      value: 100.0
+    - type: euclidean_accuracy
+      value: 59.599999999999994
+    - type: euclidean_accuracy_threshold
+      value: 200.82547664642334
+    - type: euclidean_ap
+      value: 57.19737488445163
+    - type: euclidean_f1
+      value: 62.444061962134256
+    - type: euclidean_f1_threshold
+      value: 1538.8837814331055
+    - type: euclidean_precision
+      value: 45.3953953953954
+    - type: euclidean_recall
+      value: 100.0
+    - type: main_score
+      value: 57.31662248806265
+    - type: manhattan_accuracy
+      value: 59.550000000000004
+    - type: manhattan_accuracy_threshold
+      value: 5016.501617431641
+    - type: manhattan_ap
+      value: 57.089959907945065
+    - type: manhattan_f1
+      value: 62.444061962134256
+    - type: manhattan_f1_threshold
+      value: 37523.53515625
+    - type: manhattan_precision
+      value: 45.3953953953954
+    - type: manhattan_recall
+      value: 100.0
+    - type: max_accuracy
+      value: 59.699999999999996
+    - type: max_ap
+      value: 57.31662248806265
+    - type: max_f1
+      value: 62.487082328625554
+    - type: max_precision
+      value: 45.44088176352705
+    - type: max_recall
+      value: 100.0
+    - type: similarity_accuracy
+      value: 59.699999999999996
+    - type: similarity_accuracy_threshold
+      value: 99.55930709838867
+    - type: similarity_ap
+      value: 57.31662248806265
+    - type: similarity_f1
+      value: 62.444061962134256
+    - type: similarity_f1_threshold
+      value: 74.75898265838623
+    - type: similarity_precision
+      value: 45.3953953953954
+    - type: similarity_recall
+      value: 100.0
+    task:
+      type: PairClassification
+  - dataset:
+      config: fr
+      name: MTEB PawsXPairClassification (fr)
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+      split: test
+      type: google-research-datasets/paws-x
+    metrics:
+    - type: cosine_accuracy
+      value: 61.150000000000006
+    - type: cosine_accuracy_threshold
+      value: 99.36153888702393
+    - type: cosine_ap
+      value: 59.43845317938599
+    - type: cosine_f1
+      value: 62.51298026998961
+    - type: cosine_f1_threshold
+      value: 76.77866220474243
+    - type: cosine_precision
+      value: 45.468277945619334
+    - type: cosine_recall
+      value: 100.0
+    - type: dot_accuracy
+      value: 55.75
+    - type: dot_accuracy_threshold
+      value: 48931.55212402344
+    - type: dot_ap
+      value: 50.15949290538757
+    - type: dot_f1
+      value: 62.53462603878117
+    - type: dot_f1_threshold
+      value: 34415.7958984375
+    - type: dot_precision
+      value: 45.4911838790932
+    - type: dot_recall
+      value: 100.0
+    - type: euclidean_accuracy
+      value: 61.050000000000004
+    - type: euclidean_accuracy_threshold
+      value: 240.8097267150879
+    - type: euclidean_ap
+      value: 59.367971294226216
+    - type: euclidean_f1
+      value: 62.51298026998961
+    - type: euclidean_f1_threshold
+      value: 1444.132423400879
+    - type: euclidean_precision
+      value: 45.468277945619334
+    - type: euclidean_recall
+      value: 100.0
+    - type: main_score
+      value: 59.43845317938599
+    - type: manhattan_accuracy
+      value: 60.95
+    - type: manhattan_accuracy_threshold
+      value: 5701.206207275391
+    - type: manhattan_ap
+      value: 59.30094096378774
+    - type: manhattan_f1
+      value: 62.53462603878117
+    - type: manhattan_f1_threshold
+      value: 33445.672607421875
+    - type: manhattan_precision
+      value: 45.4911838790932
+    - type: manhattan_recall
+      value: 100.0
+    - type: max_accuracy
+      value: 61.150000000000006
+    - type: max_ap
+      value: 59.43845317938599
+    - type: max_f1
+      value: 62.53462603878117
+    - type: max_precision
+      value: 45.4911838790932
+    - type: max_recall
+      value: 100.0
+    - type: similarity_accuracy
+      value: 61.150000000000006
+    - type: similarity_accuracy_threshold
+      value: 99.36153888702393
+    - type: similarity_ap
+      value: 59.43845317938599
+    - type: similarity_f1
+      value: 62.51298026998961
+    - type: similarity_f1_threshold
+      value: 76.77866220474243
+    - type: similarity_precision
+      value: 45.468277945619334
+    - type: similarity_recall
+      value: 100.0
+    task:
+      type: PairClassification
+  - dataset:
+      config: zh
+      name: MTEB PawsXPairClassification (zh)
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+      split: test
+      type: google-research-datasets/paws-x
+    metrics:
+    - type: cosine_accuracy
+      value: 58.85
+    - type: cosine_accuracy_threshold
+      value: 99.73838329315186
+    - type: cosine_ap
+      value: 54.66913160570546
+    - type: cosine_f1
+      value: 62.32136632973162
+    - type: cosine_f1_threshold
+      value: 76.4499306678772
+    - type: cosine_precision
+      value: 45.265822784810126
+    - type: cosine_recall
+      value: 100.0
+    - type: dot_accuracy
+      value: 56.25
+    - type: dot_accuracy_threshold
+      value: 47351.9287109375
+    - type: dot_ap
+      value: 48.5266232989438
+    - type: dot_f1
+      value: 62.277951933124356
+    - type: dot_f1_threshold
+      value: 31325.28076171875
+    - type: dot_precision
+      value: 45.220030349013655
+    - type: dot_recall
+      value: 100.0
+    - type: euclidean_accuracy
+      value: 58.9
+    - type: euclidean_accuracy_threshold
+      value: 144.24468278884888
+    - type: euclidean_ap
+      value: 54.66981490353506
+    - type: euclidean_f1
+      value: 62.32136632973162
+    - type: euclidean_f1_threshold
+      value: 1484.908676147461
+    - type: euclidean_precision
+      value: 45.265822784810126
+    - type: euclidean_recall
+      value: 100.0
+    - type: main_score
+      value: 54.66981490353506
+    - type: manhattan_accuracy
+      value: 58.9
+    - type: manhattan_accuracy_threshold
+      value: 3586.785125732422
+    - type: manhattan_ap
+      value: 54.668355260247736
+    - type: manhattan_f1
+      value: 62.32136632973162
+    - type: manhattan_f1_threshold
+      value: 36031.22863769531
+    - type: manhattan_precision
+      value: 45.265822784810126
+    - type: manhattan_recall
+      value: 100.0
+    - type: max_accuracy
+      value: 58.9
+    - type: max_ap
+      value: 54.66981490353506
+    - type: max_f1
+      value: 62.32136632973162
+    - type: max_precision
+      value: 45.265822784810126
+    - type: max_recall
+      value: 100.0
+    - type: similarity_accuracy
+      value: 58.85
+    - type: similarity_accuracy_threshold
+      value: 99.73838329315186
+    - type: similarity_ap
+      value: 54.66913160570546
+    - type: similarity_f1
+      value: 62.32136632973162
+    - type: similarity_f1_threshold
+      value: 76.4499306678772
+    - type: similarity_precision
+      value: 45.265822784810126
+    - type: similarity_recall
+      value: 100.0
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB PolEmo2.0-IN (default)
+      revision: d90724373c70959f17d2331ad51fb60c71176b03
+      split: test
+      type: PL-MTEB/polemo2_in
+    metrics:
+    - type: accuracy
+      value: 83.75346260387812
+    - type: f1
+      value: 81.98304891214909
+    - type: f1_weighted
+      value: 84.29623200830078
+    - type: main_score
+      value: 83.75346260387812
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB PolEmo2.0-OUT (default)
+      revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
+      split: test
+      type: PL-MTEB/polemo2_out
+    metrics:
+    - type: accuracy
+      value: 66.53846153846153
+    - type: f1
+      value: 52.71826064368638
+    - type: f1_weighted
+      value: 69.10010124630334
+    - type: main_score
+      value: 66.53846153846153
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB PPC
+      revision: None
+      split: test
+      type: PL-MTEB/ppc-pairclassification
+    metrics:
+    - type: cosine_accuracy
+      value: 81.8
+    - type: cosine_accuracy_threshold
+      value: 90.47793745994568
+    - type: cosine_ap
+      value: 91.42490266080884
+    - type: cosine_f1
+      value: 85.4632587859425
+    - type: cosine_f1_threshold
+      value: 90.47793745994568
+    - type: cosine_precision
+      value: 82.56172839506173
+    - type: cosine_recall
+      value: 88.57615894039735
+    - type: dot_accuracy
+      value: 74.6
+    - type: dot_accuracy_threshold
+      value: 42102.23693847656
+    - type: dot_ap
+      value: 86.20060009096979
+    - type: dot_f1
+      value: 80.02842928216063
+    - type: dot_f1_threshold
+      value: 38970.16906738281
+    - type: dot_precision
+      value: 70.1120797011208
+    - type: dot_recall
+      value: 93.21192052980133
+    - type: euclidean_accuracy
+      value: 81.5
+    - type: euclidean_accuracy_threshold
+      value: 880.433464050293
+    - type: euclidean_ap
+      value: 91.33143477982087
+    - type: euclidean_f1
+      value: 85.44600938967135
+    - type: euclidean_f1_threshold
+      value: 964.0384674072266
+    - type: euclidean_precision
+      value: 81.00890207715133
+    - type: euclidean_recall
+      value: 90.39735099337747
+    - type: main_score
+      value: 91.42490266080884
+    - type: manhattan_accuracy
+      value: 81.3
+    - type: manhattan_accuracy_threshold
+      value: 22100.830078125
+    - type: manhattan_ap
+      value: 91.25996158651282
+    - type: manhattan_f1
+      value: 85.38102643856921
+    - type: manhattan_f1_threshold
+      value: 24043.515014648438
+    - type: manhattan_precision
+      value: 80.49853372434018
+    - type: manhattan_recall
+      value: 90.89403973509934
+    - type: max_accuracy
+      value: 81.8
+    - type: max_ap
+      value: 91.42490266080884
+    - type: max_f1
+      value: 85.4632587859425
+    - type: max_precision
+      value: 82.56172839506173
+    - type: max_recall
+      value: 93.21192052980133
+    - type: similarity_accuracy
+      value: 81.8
+    - type: similarity_accuracy_threshold
+      value: 90.47793745994568
+    - type: similarity_ap
+      value: 91.42490266080884
+    - type: similarity_f1
+      value: 85.4632587859425
+    - type: similarity_f1_threshold
+      value: 90.47793745994568
+    - type: similarity_precision
+      value: 82.56172839506173
+    - type: similarity_recall
+      value: 88.57615894039735
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval (default)
+      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+      split: test
+      type: mteb/quora
+    metrics:
+    - type: map_at_1
+      value: 71.419
+    - type: map_at_10
+      value: 85.542
+    - type: map_at_100
+      value: 86.161
+    - type: map_at_1000
+      value: 86.175
+    - type: map_at_20
+      value: 85.949
+    - type: map_at_3
+      value: 82.623
+    - type: map_at_5
+      value: 84.5
+    - type: mrr_at_1
+      value: 82.27
+    - type: mrr_at_10
+      value: 88.21900000000001
+    - type: mrr_at_100
+      value: 88.313
+    - type: mrr_at_1000
+      value: 88.31400000000001
+    - type: mrr_at_20
+      value: 88.286
+    - type: mrr_at_3
+      value: 87.325
+    - type: mrr_at_5
+      value: 87.97500000000001
+    - type: ndcg_at_1
+      value: 82.3
+    - type: ndcg_at_10
+      value: 89.088
+    - type: ndcg_at_100
+      value: 90.217
+    - type: ndcg_at_1000
+      value: 90.29700000000001
+    - type: ndcg_at_20
+      value: 89.697
+    - type: ndcg_at_3
+      value: 86.435
+    - type: ndcg_at_5
+      value: 87.966
+    - type: precision_at_1
+      value: 82.3
+    - type: precision_at_10
+      value: 13.527000000000001
+    - type: precision_at_100
+      value: 1.537
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_20
+      value: 7.165000000000001
+    - type: precision_at_3
+      value: 37.92
+    - type: precision_at_5
+      value: 24.914
+    - type: recall_at_1
+      value: 71.419
+    - type: recall_at_10
+      value: 95.831
+    - type: recall_at_100
+      value: 99.64
+    - type: recall_at_1000
+      value: 99.988
+    - type: recall_at_20
+      value: 97.76599999999999
+    - type: recall_at_3
+      value: 88.081
+    - type: recall_at_5
+      value: 92.50500000000001
+    - type: main_score
+      value: 89.088
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RUParaPhraserSTS (default)
+      revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
+      split: test
+      type: merionum/ru_paraphraser
+    metrics:
+    - type: cosine_pearson
+      value: 67.91177744712421
+    - type: cosine_spearman
+      value: 76.77113726753656
+    - type: euclidean_pearson
+      value: 73.81454206068638
+    - type: euclidean_spearman
+      value: 76.92529493599028
+    - type: main_score
+      value: 76.77113726753656
+    - type: manhattan_pearson
+      value: 73.81690454439168
+    - type: manhattan_spearman
+      value: 76.87333776705002
+    - type: pearson
+      value: 67.91177744712421
+    - type: spearman
+      value: 76.77113726753656
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB RedditClustering (default)
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: main_score
+      value: 55.39924225216962
+    - type: v_measure
+      value: 55.39924225216962
+    - type: v_measure_std
+      value: 4.723802279292467
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P (default)
+      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: main_score
+      value: 62.87465161304012
+    - type: v_measure
+      value: 62.87465161304012
+    - type: v_measure_std
+      value: 12.082670914488473
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RiaNewsRetrieval (default)
+      revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
+      split: test
+      type: ai-forever/ria-news-retrieval
+    metrics:
+    - type: main_score
+      value: 79.209
+    - type: map_at_1
+      value: 67.33
+    - type: map_at_10
+      value: 75.633
+    - type: map_at_100
+      value: 75.897
+    - type: map_at_1000
+      value: 75.907
+    - type: map_at_20
+      value: 75.804
+    - type: map_at_3
+      value: 74.2
+    - type: map_at_5
+      value: 75.13300000000001
+    - type: mrr_at_1
+      value: 67.31
+    - type: mrr_at_10
+      value: 75.62709126984095
+    - type: mrr_at_100
+      value: 75.89105697041113
+    - type: mrr_at_1000
+      value: 75.90115653883124
+    - type: mrr_at_20
+      value: 75.79802332308172
+    - type: mrr_at_3
+      value: 74.19499999999961
+    - type: mrr_at_5
+      value: 75.12849999999939
+    - type: nauc_map_at_1000_diff1
+      value: 74.30304869630591
+    - type: nauc_map_at_1000_max
+      value: 36.477146725784046
+    - type: nauc_map_at_1000_std
+      value: -20.862772498461723
+    - type: nauc_map_at_100_diff1
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+    - type: nauc_map_at_100_max
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+    - type: nauc_map_at_100_std
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+    - type: nauc_map_at_10_diff1
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+    - type: nauc_map_at_10_std
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+    - type: recall_at_5
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+      name: MTEB RuReviewsClassification (default)
+      revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
+      split: test
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+      name: MTEB RuSTSBenchmarkSTS (default)
+      revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
+      split: test
+      type: ai-forever/ru-stsbenchmark-sts
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+    - type: pearson
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+    - type: spearman
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+  - dataset:
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+      name: MTEB RuSciBenchGRNTIClassification (default)
+      revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
+      split: test
+      type: ai-forever/ru-scibench-grnti-classification
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+    - type: f1_weighted
+      value: 58.903615560240866
+    - type: main_score
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+    task:
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+  - dataset:
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+      name: MTEB RuSciBenchGRNTIClusteringP2P (default)
+      revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
+      split: test
+      type: ai-forever/ru-scibench-grnti-classification
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+    - type: v_measure_std
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+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RuSciBenchOECDClassification (default)
+      revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
+      split: test
+      type: ai-forever/ru-scibench-oecd-classification
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+    - type: main_score
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+    task:
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+  - dataset:
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+      name: MTEB RuSciBenchOECDClusteringP2P (default)
+      revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
+      split: test
+      type: ai-forever/ru-scibench-oecd-classification
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+  - dataset:
+      config: default
+      name: MTEB SCIDOCS (default)
+      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
+      split: test
+      type: mteb/scidocs
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+      value: 23.799999999999997
+    - type: ndcg_at_10
+      value: 19.811
+    - type: ndcg_at_100
+      value: 28.108
+    - type: ndcg_at_1000
+      value: 33.1
+    - type: ndcg_at_20
+      value: 22.980999999999998
+    - type: ndcg_at_3
+      value: 19.153000000000002
+    - type: ndcg_at_5
+      value: 16.408
+    - type: precision_at_1
+      value: 23.799999999999997
+    - type: precision_at_10
+      value: 10.16
+    - type: precision_at_100
+      value: 2.1999999999999997
+    - type: precision_at_1000
+      value: 0.34099999999999997
+    - type: precision_at_20
+      value: 6.915
+    - type: precision_at_3
+      value: 17.8
+    - type: precision_at_5
+      value: 14.14
+    - type: recall_at_1
+      value: 4.843
+    - type: recall_at_10
+      value: 20.595
+    - type: recall_at_100
+      value: 44.66
+    - type: recall_at_1000
+      value: 69.152
+    - type: recall_at_20
+      value: 28.04
+    - type: recall_at_3
+      value: 10.833
+    - type: recall_at_5
+      value: 14.346999999999998
+    - type: main_score
+      value: 19.811
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-E-PL (default)
+      revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
+      split: test
+      type: PL-MTEB/sicke-pl-pairclassification
+    metrics:
+    - type: cosine_accuracy
+      value: 80.90093762739502
+    - type: cosine_accuracy_threshold
+      value: 94.40930485725403
+    - type: cosine_ap
+      value: 71.15400909912427
+    - type: cosine_f1
+      value: 66.8213457076566
+    - type: cosine_f1_threshold
+      value: 91.53673648834229
+    - type: cosine_precision
+      value: 62.4922504649721
+    - type: cosine_recall
+      value: 71.7948717948718
+    - type: dot_accuracy
+      value: 78.41418671015083
+    - type: dot_accuracy_threshold
+      value: 42924.45068359375
+    - type: dot_ap
+      value: 63.34003025365763
+    - type: dot_f1
+      value: 62.518258837277244
+    - type: dot_f1_threshold
+      value: 40900.738525390625
+    - type: dot_precision
+      value: 52.99653293709758
+    - type: dot_recall
+      value: 76.21082621082621
+    - type: euclidean_accuracy
+      value: 80.67672238075826
+    - type: euclidean_accuracy_threshold
+      value: 696.0524559020996
+    - type: euclidean_ap
+      value: 70.88762835990224
+    - type: euclidean_f1
+      value: 66.711051930759
+    - type: euclidean_f1_threshold
+      value: 878.5581588745117
+    - type: euclidean_precision
+      value: 62.625
+    - type: euclidean_recall
+      value: 71.36752136752136
+    - type: main_score
+      value: 71.15400909912427
+    - type: manhattan_accuracy
+      value: 80.65633917651854
+    - type: manhattan_accuracy_threshold
+      value: 17277.72674560547
+    - type: manhattan_ap
+      value: 70.67105336611716
+    - type: manhattan_f1
+      value: 66.51346027577151
+    - type: manhattan_f1_threshold
+      value: 21687.957763671875
+    - type: manhattan_precision
+      value: 61.69305724725944
+    - type: manhattan_recall
+      value: 72.15099715099716
+    - type: max_accuracy
+      value: 80.90093762739502
+    - type: max_ap
+      value: 71.15400909912427
+    - type: max_f1
+      value: 66.8213457076566
+    - type: max_precision
+      value: 62.625
+    - type: max_recall
+      value: 76.21082621082621
+    - type: similarity_accuracy
+      value: 80.90093762739502
+    - type: similarity_accuracy_threshold
+      value: 94.40930485725403
+    - type: similarity_ap
+      value: 71.15400909912427
+    - type: similarity_f1
+      value: 66.8213457076566
+    - type: similarity_f1_threshold
+      value: 91.53673648834229
+    - type: similarity_precision
+      value: 62.4922504649721
+    - type: similarity_recall
+      value: 71.7948717948718
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB SICK-R (default)
+      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cosine_pearson
+      value: 92.3339946866199
+    - type: cosine_spearman
+      value: 89.61697355115497
+    - type: euclidean_pearson
+      value: 90.3264916449669
+    - type: euclidean_spearman
+      value: 89.36270451308866
+    - type: main_score
+      value: 89.61697355115497
+    - type: manhattan_pearson
+      value: 90.18909339052534
+    - type: manhattan_spearman
+      value: 89.28337093097377
+    - type: pearson
+      value: 92.3339946866199
+    - type: spearman
+      value: 89.61697355115497
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SICK-R-PL (default)
+      revision: fd5c2441b7eeff8676768036142af4cfa42c1339
+      split: test
+      type: PL-MTEB/sickr-pl-sts
+    metrics:
+    - type: cosine_pearson
+      value: 85.27883048457821
+    - type: cosine_spearman
+      value: 80.53204892678619
+    - type: euclidean_pearson
+      value: 82.78520705216168
+    - type: euclidean_spearman
+      value: 80.27848359873212
+    - type: main_score
+      value: 80.53204892678619
+    - type: manhattan_pearson
+      value: 82.63270640583454
+    - type: manhattan_spearman
+      value: 80.21507977473146
+    - type: pearson
+      value: 85.27883048457821
+    - type: spearman
+      value: 80.53204892678619
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SICKFr (default)
+      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
+      split: test
+      type: Lajavaness/SICK-fr
+    metrics:
+    - type: cosine_pearson
+      value: 88.77029361817212
+    - type: cosine_spearman
+      value: 83.9453600346894
+    - type: euclidean_pearson
+      value: 85.85331086208573
+    - type: euclidean_spearman
+      value: 83.70852031985308
+    - type: main_score
+      value: 83.9453600346894
+    - type: manhattan_pearson
+      value: 85.66222265885914
+    - type: manhattan_spearman
+      value: 83.60833111525962
+    - type: pearson
+      value: 88.77029361817212
+    - type: spearman
+      value: 83.9453600346894
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12 (default)
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cosine_pearson
+      value: 88.76435859522375
+    - type: cosine_spearman
+      value: 82.43768167804375
+    - type: euclidean_pearson
+      value: 87.43566183874832
+    - type: euclidean_spearman
+      value: 82.82166873757507
+    - type: main_score
+      value: 82.43768167804375
+    - type: manhattan_pearson
+      value: 87.39450871380951
+    - type: manhattan_spearman
+      value: 82.89253043430163
+    - type: pearson
+      value: 88.76435859522375
+    - type: spearman
+      value: 82.43768167804375
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13 (default)
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cosine_pearson
+      value: 88.86627241652141
+    - type: cosine_spearman
+      value: 89.49011599120688
+    - type: euclidean_pearson
+      value: 89.3314120073772
+    - type: euclidean_spearman
+      value: 89.8226502776963
+    - type: main_score
+      value: 89.49011599120688
+    - type: manhattan_pearson
+      value: 89.2252179076963
+    - type: manhattan_spearman
+      value: 89.74573844021225
+    - type: pearson
+      value: 88.86627241652141
+    - type: spearman
+      value: 89.49011599120688
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14 (default)
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cosine_pearson
+      value: 87.22891405215968
+    - type: cosine_spearman
+      value: 84.9467188157614
+    - type: euclidean_pearson
+      value: 87.20330004726237
+    - type: euclidean_spearman
+      value: 85.34806059461808
+    - type: main_score
+      value: 84.9467188157614
+    - type: manhattan_pearson
+      value: 87.15224666107623
+    - type: manhattan_spearman
+      value: 85.34596898699708
+    - type: pearson
+      value: 87.22891405215968
+    - type: spearman
+      value: 84.9467188157614
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15 (default)
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cosine_pearson
+      value: 88.14066430111033
+    - type: cosine_spearman
+      value: 89.31337445552545
+    - type: euclidean_pearson
+      value: 89.08039335366983
+    - type: euclidean_spearman
+      value: 89.6658762856415
+    - type: main_score
+      value: 89.31337445552545
+    - type: manhattan_pearson
+      value: 89.08057438154486
+    - type: manhattan_spearman
+      value: 89.68673984203022
+    - type: pearson
+      value: 88.14066430111033
+    - type: spearman
+      value: 89.31337445552545
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16 (default)
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cosine_pearson
+      value: 85.14908856657084
+    - type: cosine_spearman
+      value: 86.84648320786727
+    - type: euclidean_pearson
+      value: 86.11454713131947
+    - type: euclidean_spearman
+      value: 86.77738862047961
+    - type: main_score
+      value: 86.84648320786727
+    - type: manhattan_pearson
+      value: 86.07804821916372
+    - type: manhattan_spearman
+      value: 86.78676064310474
+    - type: pearson
+      value: 85.14908856657084
+    - type: spearman
+      value: 86.84648320786727
+    task:
+      type: STS
+  - dataset:
+      config: en-en
+      name: MTEB STS17 (en-en)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 89.61633502468356
+    - type: cosine_spearman
+      value: 89.99772663224805
+    - type: euclidean_pearson
+      value: 90.14056501501044
+    - type: euclidean_spearman
+      value: 90.04496896837503
+    - type: main_score
+      value: 89.99772663224805
+    - type: manhattan_pearson
+      value: 90.08964860311801
+    - type: manhattan_spearman
+      value: 90.00091712362196
+    - type: pearson
+      value: 89.61633502468356
+    - type: spearman
+      value: 89.99772663224805
+    task:
+      type: STS
+  - dataset:
+      config: es-en
+      name: MTEB STS17 (es-en)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 86.44548026840202
+    - type: cosine_spearman
+      value: 87.26263108768539
+    - type: euclidean_pearson
+      value: 86.42844593583838
+    - type: euclidean_spearman
+      value: 86.89388428664364
+    - type: main_score
+      value: 87.26263108768539
+    - type: manhattan_pearson
+      value: 86.47186940800881
+    - type: manhattan_spearman
+      value: 87.02163091089946
+    - type: pearson
+      value: 86.44548026840202
+    - type: spearman
+      value: 87.26263108768539
+    task:
+      type: STS
+  - dataset:
+      config: en-de
+      name: MTEB STS17 (en-de)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 87.89345132532758
+    - type: cosine_spearman
+      value: 87.96246221327699
+    - type: euclidean_pearson
+      value: 88.49013032701419
+    - type: euclidean_spearman
+      value: 87.81981265317344
+    - type: main_score
+      value: 87.96246221327699
+    - type: manhattan_pearson
+      value: 88.31360914178538
+    - type: manhattan_spearman
+      value: 87.62734530005075
+    - type: pearson
+      value: 87.89345132532758
+    - type: spearman
+      value: 87.96246221327699
+    task:
+      type: STS
+  - dataset:
+      config: es-es
+      name: MTEB STS17 (es-es)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 88.4084678497171
+    - type: cosine_spearman
+      value: 88.77640638748285
+    - type: euclidean_pearson
+      value: 89.60124312475843
+    - type: euclidean_spearman
+      value: 88.4321442688528
+    - type: main_score
+      value: 88.77640638748285
+    - type: manhattan_pearson
+      value: 89.62375118021299
+    - type: manhattan_spearman
+      value: 88.46998118661577
+    - type: pearson
+      value: 88.4084678497171
+    - type: spearman
+      value: 88.77640638748285
+    task:
+      type: STS
+  - dataset:
+      config: fr-en
+      name: MTEB STS17 (fr-en)
+      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 87.30688801326498
+    - type: cosine_spearman
+      value: 87.55684697258378
+    - type: euclidean_pearson
+      value: 87.89672951056794
+    - type: euclidean_spearman
+      value: 87.28050429201674
+    - type: main_score
+      value: 87.55684697258378
+    - type: manhattan_pearson
+      value: 87.74292745320572
+    - type: manhattan_spearman
+      value: 87.16383993876582
+    - type: pearson
+      value: 87.30688801326498
+    - type: spearman
+      value: 87.55684697258378
+    task:
+      type: STS
+  - dataset:
+      config: zh-en
+      name: MTEB STS22 (zh-en)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 73.46180375170147
+    - type: cosine_spearman
+      value: 73.39559590127081
+    - type: euclidean_pearson
+      value: 73.72613901293681
+    - type: euclidean_spearman
+      value: 71.85465165176795
+    - type: main_score
+      value: 73.39559590127081
+    - type: manhattan_pearson
+      value: 73.07859140869076
+    - type: manhattan_spearman
+      value: 71.22047343718893
+    - type: pearson
+      value: 73.46180375170147
+    - type: spearman
+      value: 73.39559590127081
+    task:
+      type: STS
+  - dataset:
+      config: zh
+      name: MTEB STS22 (zh)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 62.47531620842637
+    - type: cosine_spearman
+      value: 66.22504667157702
+    - type: euclidean_pearson
+      value: 66.76201254783692
+    - type: euclidean_spearman
+      value: 66.86115760269463
+    - type: main_score
+      value: 66.22504667157702
+    - type: manhattan_pearson
+      value: 66.73847836793489
+    - type: manhattan_spearman
+      value: 66.7677116377695
+    - type: pearson
+      value: 62.47531620842637
+    - type: spearman
+      value: 66.22504667157702
+    task:
+      type: STS
+  - dataset:
+      config: es
+      name: MTEB STS22 (es)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 69.89707002436481
+    - type: cosine_spearman
+      value: 72.2054865735116
+    - type: euclidean_pearson
+      value: 71.81856615570756
+    - type: euclidean_spearman
+      value: 72.72593304629407
+    - type: main_score
+      value: 72.2054865735116
+    - type: manhattan_pearson
+      value: 72.00362684700072
+    - type: manhattan_spearman
+      value: 72.62783534769964
+    - type: pearson
+      value: 69.89707002436481
+    - type: spearman
+      value: 72.2054865735116
+    task:
+      type: STS
+  - dataset:
+      config: fr
+      name: MTEB STS22 (fr)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 81.59623734395916
+    - type: cosine_spearman
+      value: 83.28946105111358
+    - type: euclidean_pearson
+      value: 79.377330171466
+    - type: euclidean_spearman
+      value: 81.81029781662205
+    - type: main_score
+      value: 83.28946105111358
+    - type: manhattan_pearson
+      value: 78.96970881689698
+    - type: manhattan_spearman
+      value: 81.91773236079703
+    - type: pearson
+      value: 81.59623734395916
+    - type: spearman
+      value: 83.28946105111358
+    task:
+      type: STS
+  - dataset:
+      config: de-fr
+      name: MTEB STS22 (de-fr)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 55.03825643126142
+    - type: cosine_spearman
+      value: 58.25792501780429
+    - type: euclidean_pearson
+      value: 50.38007603973409
+    - type: euclidean_spearman
+      value: 59.39961789383097
+    - type: main_score
+      value: 58.25792501780429
+    - type: manhattan_pearson
+      value: 50.518568927999155
+    - type: manhattan_spearman
+      value: 59.84185466003894
+    - type: pearson
+      value: 55.03825643126142
+    - type: spearman
+      value: 58.25792501780429
+    task:
+      type: STS
+  - dataset:
+      config: pl-en
+      name: MTEB STS22 (pl-en)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 77.77233721490776
+    - type: cosine_spearman
+      value: 76.17596588017625
+    - type: euclidean_pearson
+      value: 74.47600468156611
+    - type: euclidean_spearman
+      value: 72.61278728057012
+    - type: main_score
+      value: 76.17596588017625
+    - type: manhattan_pearson
+      value: 74.48118910099699
+    - type: manhattan_spearman
+      value: 73.33167419101696
+    - type: pearson
+      value: 77.77233721490776
+    - type: spearman
+      value: 76.17596588017625
+    task:
+      type: STS
+  - dataset:
+      config: pl
+      name: MTEB STS22 (pl)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 42.87453608131507
+    - type: cosine_spearman
+      value: 45.137849894401185
+    - type: euclidean_pearson
+      value: 31.66964197694796
+    - type: euclidean_spearman
+      value: 44.1014900837869
+    - type: main_score
+      value: 45.137849894401185
+    - type: manhattan_pearson
+      value: 31.007199259384745
+    - type: manhattan_spearman
+      value: 43.48181523288926
+    - type: pearson
+      value: 42.87453608131507
+    - type: spearman
+      value: 45.137849894401185
+    task:
+      type: STS
+  - dataset:
+      config: en
+      name: MTEB STS22 (en)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 66.87400150638176
+    - type: cosine_spearman
+      value: 67.27861354834066
+    - type: euclidean_pearson
+      value: 66.81789582140216
+    - type: euclidean_spearman
+      value: 66.44220479858708
+    - type: main_score
+      value: 67.27861354834066
+    - type: manhattan_pearson
+      value: 66.92509859033235
+    - type: manhattan_spearman
+      value: 66.46841124185076
+    - type: pearson
+      value: 66.87400150638176
+    - type: spearman
+      value: 67.27861354834066
+    task:
+      type: STS
+  - dataset:
+      config: ru
+      name: MTEB STS22 (ru)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 61.819804551576084
+    - type: cosine_spearman
+      value: 65.0864146772135
+    - type: euclidean_pearson
+      value: 62.518151090361876
+    - type: euclidean_spearman
+      value: 65.13608138548017
+    - type: main_score
+      value: 65.0864146772135
+    - type: manhattan_pearson
+      value: 62.51413246915267
+    - type: manhattan_spearman
+      value: 65.19077543064323
+    - type: pearson
+      value: 61.819804551576084
+    - type: spearman
+      value: 65.0864146772135
+    task:
+      type: STS
+  - dataset:
+      config: de
+      name: MTEB STS22 (de)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 54.85728696035389
+    - type: cosine_spearman
+      value: 61.60906359227576
+    - type: euclidean_pearson
+      value: 52.57582587901851
+    - type: euclidean_spearman
+      value: 61.41823097598308
+    - type: main_score
+      value: 61.60906359227576
+    - type: manhattan_pearson
+      value: 52.500978361080506
+    - type: manhattan_spearman
+      value: 61.30365596659758
+    - type: pearson
+      value: 54.85728696035389
+    - type: spearman
+      value: 61.60906359227576
+    task:
+      type: STS
+  - dataset:
+      config: fr-pl
+      name: MTEB STS22 (fr-pl)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 67.68016005631422
+    - type: cosine_spearman
+      value: 84.51542547285167
+    - type: euclidean_pearson
+      value: 66.19871164667245
+    - type: euclidean_spearman
+      value: 73.24670207647144
+    - type: main_score
+      value: 84.51542547285167
+    - type: manhattan_pearson
+      value: 67.0443525268974
+    - type: manhattan_spearman
+      value: 73.24670207647144
+    - type: pearson
+      value: 67.68016005631422
+    - type: spearman
+      value: 84.51542547285167
+    task:
+      type: STS
+  - dataset:
+      config: de-pl
+      name: MTEB STS22 (de-pl)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 47.49467414030747
+    - type: cosine_spearman
+      value: 56.81512095681289
+    - type: euclidean_pearson
+      value: 48.42860221765214
+    - type: euclidean_spearman
+      value: 58.63197306329092
+    - type: main_score
+      value: 56.81512095681289
+    - type: manhattan_pearson
+      value: 48.39594959260441
+    - type: manhattan_spearman
+      value: 58.63197306329092
+    - type: pearson
+      value: 47.49467414030747
+    - type: spearman
+      value: 56.81512095681289
+    task:
+      type: STS
+  - dataset:
+      config: es-en
+      name: MTEB STS22 (es-en)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 76.8364678896155
+    - type: cosine_spearman
+      value: 78.45516413087114
+    - type: euclidean_pearson
+      value: 78.62779318576634
+    - type: euclidean_spearman
+      value: 78.88760695649488
+    - type: main_score
+      value: 78.45516413087114
+    - type: manhattan_pearson
+      value: 78.62131335760031
+    - type: manhattan_spearman
+      value: 78.81861844200388
+    - type: pearson
+      value: 76.8364678896155
+    - type: spearman
+      value: 78.45516413087114
+    task:
+      type: STS
+  - dataset:
+      config: de-en
+      name: MTEB STS22 (de-en)
+      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cosine_pearson
+      value: 65.16640313911604
+    - type: cosine_spearman
+      value: 60.887608967403914
+    - type: euclidean_pearson
+      value: 67.49902244990913
+    - type: euclidean_spearman
+      value: 59.2458787136538
+    - type: main_score
+      value: 60.887608967403914
+    - type: manhattan_pearson
+      value: 67.34313506388378
+    - type: manhattan_spearman
+      value: 59.05283429200166
+    - type: pearson
+      value: 65.16640313911604
+    - type: spearman
+      value: 60.887608967403914
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSB (default)
+      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
+      split: test
+      type: C-MTEB/STSB
+    metrics:
+    - type: cosine_pearson
+      value: 81.5092853013241
+    - type: cosine_spearman
+      value: 83.54005474244292
+    - type: euclidean_pearson
+      value: 83.7246578378554
+    - type: euclidean_spearman
+      value: 84.46767551087716
+    - type: main_score
+      value: 83.54005474244292
+    - type: manhattan_pearson
+      value: 83.65922665594636
+    - type: manhattan_spearman
+      value: 84.42431449101848
+    - type: pearson
+      value: 81.5092853013241
+    - type: spearman
+      value: 83.54005474244292
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark (default)
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cosine_pearson
+      value: 87.70246866744966
+    - type: cosine_spearman
+      value: 89.44070045346106
+    - type: euclidean_pearson
+      value: 89.56956519641007
+    - type: euclidean_spearman
+      value: 89.95830112784283
+    - type: main_score
+      value: 89.44070045346106
+    - type: manhattan_pearson
+      value: 89.48264471425145
+    - type: manhattan_spearman
+      value: 89.87900732483114
+    - type: pearson
+      value: 87.70246866744966
+    - type: spearman
+      value: 89.44070045346106
+    task:
+      type: STS
+  - dataset:
+      config: de
+      name: MTEB STSBenchmarkMultilingualSTS (de)
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_pearson
+      value: 86.83701990805217
+    - type: cosine_spearman
+      value: 87.80280785492258
+    - type: euclidean_pearson
+      value: 87.77325330043514
+    - type: euclidean_spearman
+      value: 88.3564607283144
+    - type: main_score
+      value: 87.80280785492258
+    - type: manhattan_pearson
+      value: 87.6745449945946
+    - type: manhattan_spearman
+      value: 88.30660465978795
+    - type: pearson
+      value: 86.83701990805217
+    - type: spearman
+      value: 87.80280785492258
+    task:
+      type: STS
+  - dataset:
+      config: zh
+      name: MTEB STSBenchmarkMultilingualSTS (zh)
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_pearson
+      value: 84.27751020600267
+    - type: cosine_spearman
+      value: 85.63500407412486
+    - type: euclidean_pearson
+      value: 85.21829891649696
+    - type: euclidean_spearman
+      value: 85.9384575715382
+    - type: main_score
+      value: 85.63500407412486
+    - type: manhattan_pearson
+      value: 85.10797194089801
+    - type: manhattan_spearman
+      value: 85.8770162042784
+    - type: pearson
+      value: 84.27751020600267
+    - type: spearman
+      value: 85.63500407412486
+    task:
+      type: STS
+  - dataset:
+      config: fr
+      name: MTEB STSBenchmarkMultilingualSTS (fr)
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_pearson
+      value: 86.56833656723254
+    - type: cosine_spearman
+      value: 87.4393978501382
+    - type: euclidean_pearson
+      value: 87.45171512751267
+    - type: euclidean_spearman
+      value: 88.13106516566947
+    - type: main_score
+      value: 87.4393978501382
+    - type: manhattan_pearson
+      value: 87.33010961793333
+    - type: manhattan_spearman
+      value: 88.06707425102182
+    - type: pearson
+      value: 86.56833656723254
+    - type: spearman
+      value: 87.4393978501382
+    task:
+      type: STS
+  - dataset:
+      config: pl
+      name: MTEB STSBenchmarkMultilingualSTS (pl)
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_pearson
+      value: 85.45065540325523
+    - type: cosine_spearman
+      value: 85.47881076789359
+    - type: euclidean_pearson
+      value: 85.1999493863155
+    - type: euclidean_spearman
+      value: 85.7874947669187
+    - type: main_score
+      value: 85.47881076789359
+    - type: manhattan_pearson
+      value: 85.06075305990376
+    - type: manhattan_spearman
+      value: 85.71563015639558
+    - type: pearson
+      value: 85.45065540325523
+    - type: spearman
+      value: 85.47881076789359
+    task:
+      type: STS
+  - dataset:
+      config: es
+      name: MTEB STSBenchmarkMultilingualSTS (es)
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_pearson
+      value: 87.11952824079832
+    - type: cosine_spearman
+      value: 87.9643473573153
+    - type: euclidean_pearson
+      value: 88.11750364639971
+    - type: euclidean_spearman
+      value: 88.63695109016498
+    - type: main_score
+      value: 87.9643473573153
+    - type: manhattan_pearson
+      value: 88.00294453126699
+    - type: manhattan_spearman
+      value: 88.53750241758391
+    - type: pearson
+      value: 87.11952824079832
+    - type: spearman
+      value: 87.9643473573153
+    task:
+      type: STS
+  - dataset:
+      config: ru
+      name: MTEB STSBenchmarkMultilingualSTS (ru)
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_pearson
+      value: 85.99804354414991
+    - type: cosine_spearman
+      value: 86.30252111551002
+    - type: euclidean_pearson
+      value: 86.1880652037762
+    - type: euclidean_spearman
+      value: 86.69556223944502
+    - type: main_score
+      value: 86.30252111551002
+    - type: manhattan_pearson
+      value: 86.0736400320898
+    - type: manhattan_spearman
+      value: 86.61747927593393
+    - type: pearson
+      value: 85.99804354414991
+    - type: spearman
+      value: 86.30252111551002
+    task:
+      type: STS
+  - dataset:
+      config: en
+      name: MTEB STSBenchmarkMultilingualSTS (en)
+      revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
+      split: test
+      type: mteb/stsb_multi_mt
+    metrics:
+    - type: cosine_pearson
+      value: 87.70246861738103
+    - type: cosine_spearman
+      value: 89.44070045346106
+    - type: euclidean_pearson
+      value: 89.56956518833663
+    - type: euclidean_spearman
+      value: 89.95830112784283
+    - type: main_score
+      value: 89.44070045346106
+    - type: manhattan_pearson
+      value: 89.48264470792915
+    - type: manhattan_spearman
+      value: 89.87900732483114
+    - type: pearson
+      value: 87.70246861738103
+    - type: spearman
+      value: 89.44070045346106
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR (default)
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 84.88064122814694
+    - type: mrr
+      value: 95.84832651009123
+    - type: main_score
+      value: 84.88064122814694
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact (default)
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+      split: test
+      type: mteb/scifact
+    metrics:
+    - type: map_at_1
+      value: 57.289
+    - type: map_at_10
+      value: 67.88499999999999
+    - type: map_at_100
+      value: 68.477
+    - type: map_at_1000
+      value: 68.50500000000001
+    - type: map_at_20
+      value: 68.33500000000001
+    - type: map_at_3
+      value: 65.08
+    - type: map_at_5
+      value: 67.001
+    - type: mrr_at_1
+      value: 59.667
+    - type: mrr_at_10
+      value: 68.626
+    - type: mrr_at_100
+      value: 69.082
+    - type: mrr_at_1000
+      value: 69.108
+    - type: mrr_at_20
+      value: 68.958
+    - type: mrr_at_3
+      value: 66.667
+    - type: mrr_at_5
+      value: 67.983
+    - type: ndcg_at_1
+      value: 59.667
+    - type: ndcg_at_10
+      value: 72.309
+    - type: ndcg_at_100
+      value: 74.58399999999999
+    - type: ndcg_at_1000
+      value: 75.25500000000001
+    - type: ndcg_at_20
+      value: 73.656
+    - type: ndcg_at_3
+      value: 67.791
+    - type: ndcg_at_5
+      value: 70.45
+    - type: precision_at_1
+      value: 59.667
+    - type: precision_at_10
+      value: 9.567
+    - type: precision_at_100
+      value: 1.073
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_20
+      value: 5.083
+    - type: precision_at_3
+      value: 26.333000000000002
+    - type: precision_at_5
+      value: 17.666999999999998
+    - type: recall_at_1
+      value: 57.289
+    - type: recall_at_10
+      value: 84.756
+    - type: recall_at_100
+      value: 94.5
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_20
+      value: 89.7
+    - type: recall_at_3
+      value: 73.22800000000001
+    - type: recall_at_5
+      value: 79.444
+    - type: main_score
+      value: 72.309
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SpanishNewsClusteringP2P (default)
+      revision: bf8ca8ddc5b7da4f7004720ddf99bbe0483480e6
+      split: test
+      type: jinaai/spanish_news_clustering
+    metrics:
+    - type: main_score
+      value: 45.04477709795154
+    - type: v_measure
+      value: 45.04477709795154
+    - type: v_measure_std
+      value: 0.0
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SpanishPassageRetrievalS2S (default)
+      revision: 9cddf2ce5209ade52c2115ccfa00eb22c6d3a837
+      split: test
+      type: jinaai/spanish_passage_retrieval
+    metrics:
+    - type: main_score
+      value: 69.83
+    - type: map_at_1
+      value: 15.736
+    - type: map_at_10
+      value: 52.027
+    - type: map_at_100
+      value: 65.08800000000001
+    - type: map_at_1000
+      value: 65.08800000000001
+    - type: map_at_20
+      value: 60.79900000000001
+    - type: map_at_3
+      value: 32.869
+    - type: map_at_5
+      value: 41.436
+    - type: mrr_at_1
+      value: 75.44910179640718
+    - type: mrr_at_10
+      value: 84.43446440452426
+    - type: mrr_at_100
+      value: 84.48052612723271
+    - type: mrr_at_1000
+      value: 84.48052612723271
+    - type: mrr_at_20
+      value: 84.48052612723271
+    - type: mrr_at_3
+      value: 83.13373253493013
+    - type: mrr_at_5
+      value: 84.3013972055888
+    - type: nauc_map_at_1000_diff1
+      value: 50.611540149694356
+    - type: nauc_map_at_1000_max
+      value: 2.1102430434260238
+    - type: nauc_map_at_1000_std
+      value: -18.88993521335793
+    - type: nauc_map_at_100_diff1
+      value: 50.611540149694356
+    - type: nauc_map_at_100_max
+      value: 2.1102430434260238
+    - type: nauc_map_at_100_std
+      value: -18.88993521335793
+    - type: nauc_map_at_10_diff1
+      value: 59.13518981755268
+    - type: nauc_map_at_10_max
+      value: -9.810386627392807
+    - type: nauc_map_at_10_std
+      value: -38.31810152345078
+    - type: nauc_map_at_1_diff1
+      value: 74.96782567287174
+    - type: nauc_map_at_1_max
+      value: -29.648279252607875
+    - type: nauc_map_at_1_std
+      value: -54.017459339141595
+    - type: nauc_map_at_20_diff1
+      value: 55.26694458629849
+    - type: nauc_map_at_20_max
+      value: -1.9490244535020729
+    - type: nauc_map_at_20_std
+      value: -25.22211659104076
+    - type: nauc_map_at_3_diff1
+      value: 71.67607885031732
+    - type: nauc_map_at_3_max
+      value: -25.078101661694507
+    - type: nauc_map_at_3_std
+      value: -50.55408861920259
+    - type: nauc_map_at_5_diff1
+      value: 61.50111515417668
+    - type: nauc_map_at_5_max
+      value: -16.4114670513168
+    - type: nauc_map_at_5_std
+      value: -44.391416134859135
+    - type: nauc_mrr_at_1000_diff1
+      value: 74.18848063283234
+    - type: nauc_mrr_at_1000_max
+      value: 21.929205946778005
+    - type: nauc_mrr_at_1000_std
+      value: -36.27399268489433
+    - type: nauc_mrr_at_100_diff1
+      value: 74.18848063283234
+    - type: nauc_mrr_at_100_max
+      value: 21.929205946778005
+    - type: nauc_mrr_at_100_std
+      value: -36.27399268489433
+    - type: nauc_mrr_at_10_diff1
+      value: 74.27231582268745
+    - type: nauc_mrr_at_10_max
+      value: 21.481133301135337
+    - type: nauc_mrr_at_10_std
+      value: -36.72070854872902
+    - type: nauc_mrr_at_1_diff1
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+      revision: 7b58f24536063837d644aab9a023c62199b2a612
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+      type: ai-forever/terra-pairclassification
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+    - type: similarity_precision
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+  - dataset:
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+      name: MTEB TNews (default)
+      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
+      split: validation
+      type: C-MTEB/TNews-classification
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+    task:
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+  - dataset:
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+      name: MTEB TRECCOVID (default)
+      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
+      split: test
+      type: mteb/trec-covid
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+    - type: main_score
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+    task:
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+  - dataset:
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+      name: MTEB TenKGnadClusteringP2P (default)
+      revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
+      split: test
+      type: slvnwhrl/tenkgnad-clustering-p2p
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+      value: 43.763609722295215
+    - type: v_measure
+      value: 43.763609722295215
+    - type: v_measure_std
+      value: 2.8751199473862457
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TenKGnadClusteringS2S (default)
+      revision: 6cddbe003f12b9b140aec477b583ac4191f01786
+      split: test
+      type: slvnwhrl/tenkgnad-clustering-s2s
+    metrics:
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+      value: 39.762424448504355
+    - type: v_measure
+      value: 39.762424448504355
+    - type: v_measure_std
+      value: 3.30146124979502
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ThuNewsClusteringP2P (default)
+      revision: 5798586b105c0434e4f0fe5e767abe619442cf93
+      split: test
+      type: C-MTEB/ThuNewsClusteringP2P
+    metrics:
+    - type: main_score
+      value: 63.133819258289456
+    - type: v_measure
+      value: 63.133819258289456
+    - type: v_measure_std
+      value: 1.8854253356479695
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ThuNewsClusteringS2S (default)
+      revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
+      split: test
+      type: C-MTEB/ThuNewsClusteringS2S
+    metrics:
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+      value: 58.98195851785808
+    - type: v_measure
+      value: 58.98195851785808
+    - type: v_measure_std
+      value: 1.6237600076393737
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB Touche2020 (default)
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+      split: test
+      type: mteb/touche2020
+    metrics:
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+      value: 3.3550000000000004
+    - type: map_at_10
+      value: 10.08
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+      value: 16.136
+    - type: map_at_1000
+      value: 17.605
+    - type: map_at_20
+      value: 12.561
+    - type: map_at_3
+      value: 5.641
+    - type: map_at_5
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+    - type: mrr_at_1
+      value: 46.939
+    - type: mrr_at_10
+      value: 58.152
+    - type: mrr_at_100
+      value: 58.594
+    - type: mrr_at_1000
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+    - type: mrr_at_20
+      value: 58.279
+    - type: mrr_at_3
+      value: 55.102
+    - type: mrr_at_5
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+    - type: ndcg_at_1
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+    - type: ndcg_at_10
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+    - type: ndcg_at_1000
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+    - type: ndcg_at_3
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+    - type: ndcg_at_5
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+    - type: precision_at_3
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+    - type: precision_at_5
+      value: 26.122
+    - type: recall_at_1
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+    - type: recall_at_10
+      value: 16.41
+    - type: recall_at_100
+      value: 47.272
+    - type: recall_at_1000
+      value: 83.584
+    - type: recall_at_20
+      value: 24.091
+    - type: recall_at_3
+      value: 6.8180000000000005
+    - type: recall_at_5
+      value: 9.677
+    - type: main_score
+      value: 26.298
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification (default)
+      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
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+      value: 91.2890625
+    - type: ap
+      value: 33.95547153875715
+    - type: ap_weighted
+      value: 33.95547153875715
+    - type: f1
+      value: 75.10768597556462
+    - type: f1_weighted
+      value: 92.00161208992606
+    - type: main_score
+      value: 91.2890625
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification (default)
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
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+      value: 71.3978494623656
+    - type: f1
+      value: 71.7194818511814
+    - type: f1_weighted
+      value: 71.13860187349744
+    - type: main_score
+      value: 71.3978494623656
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering (default)
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
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+      value: 52.4921688720602
+    - type: v_measure
+      value: 52.4921688720602
+    - type: v_measure_std
+      value: 0.992768152658908
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015 (default)
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
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+    - type: euclidean_accuracy
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+    - type: euclidean_accuracy_threshold
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+    - type: main_score
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+    - type: max_accuracy
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+    - type: max_precision
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+    - type: max_recall
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+    - type: similarity_accuracy
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+    - type: similarity_accuracy_threshold
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+    - type: similarity_ap
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+    - type: similarity_f1
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+    - type: similarity_precision
+      value: 61.094708153531684
+    - type: similarity_recall
+      value: 70.97625329815304
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus (default)
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
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+    - type: cosine_accuracy_threshold
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+    - type: cosine_ap
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+      name: MTEB VideoRetrieval (default)
+      revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
+      split: dev
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+      revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
+      split: test
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+      value: 44.458999999999996
+    - type: precision_at_5
+      value: 31.535000000000004
+    - type: recall_at_1
+      value: 46.444
+    - type: recall_at_10
+      value: 86.275
+    - type: recall_at_100
+      value: 98.017
+    - type: recall_at_1000
+      value: 99.8
+    - type: recall_at_20
+      value: 90.935
+    - type: recall_at_3
+      value: 70.167
+    - type: recall_at_5
+      value: 78.2
+    task:
+      type: Retrieval
+---
+
+<br><br>
+
+<p align="center">
+<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
+</p>
+
+
+<p align="center">
+<b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
+</p>
+
+<p align="center">
+<b>jina-embeddings-v3: Multilingual Embeddings With Task LoRA</b>
+</p>
+
+## Quick Start
+
+[Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings)
+
+
+## Intended Usage & Model Info
+
+
+`jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications.
+Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation), 
+this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**.
+Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.
+
+### Key Features:
+- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.
+- **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options:
+    - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks
+    - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks
+    - `separation`: Used for embeddings in clustering and re-ranking applications
+    - `classification`: Used for embeddings in classification tasks
+    - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
+- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application.
+
+### Supported Languages:
+While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: 
+**Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, 
+Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, 
+Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
+
+## Usage
+
+**<details><summary>Apply mean pooling when integrating the model.</summary>**
+<p>
+
+### Why Use Mean Pooling?
+
+Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level. 
+This approach has been shown to produce high-quality sentence embeddings.
+
+We provide an `encode` function that handles this for you automatically.
+
+However, if you're working with the model directly, outside of the `encode` function, 
+you'll need to apply mean pooling manually. Here's how you can do it:
+
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = (
+        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    )
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+        input_mask_expanded.sum(1), min=1e-9
+    )
+
+
+sentences = ["How is the weather today?", "What is the current weather like today?"]
+
+tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3")
+model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
+
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
+task = 'retrieval.query'
+task_id = model._adaptation_map[task]
+adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32)
+with torch.no_grad():
+    model_output = model(**encoded_input, adapter_mask=adapter_mask)
+
+embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
+embeddings = F.normalize(embeddings, p=2, dim=1)
+
+```
+
+</p>
+</details>
+
+The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/).
+
+Alternatively, you can use `jina-embeddings-v3` directly via Transformers package:
+```bash
+!pip install transformers torch einops
+!pip install 'numpy<2'
+```
+If you run it on a GPU that support [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100),
+
+```bash
+!pip install flash-attn --no-build-isolation
+```
+
+```python
+from transformers import AutoModel
+
+# Initialize the model
+model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
+
+texts = [
+    "Follow the white rabbit.",  # English
+    "Sigue al conejo blanco.",  # Spanish
+    "Suis le lapin blanc.",  # French
+    "跟着白兔走。",  # Chinese
+    "اتبع الأرنب الأبيض.",  # Arabic
+    "Folge dem weißen Kaninchen.",  # German
+]
+
+# When calling the `encode` function, you can choose a `task` based on the use case:
+# 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching'
+# Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used.
+embeddings = model.encode(texts, task="text-matching")
+
+# Compute similarities
+print(embeddings[0] @ embeddings[1].T)
+```
+
+By default, the model supports a maximum sequence length of 8192 tokens. 
+However, if you want to truncate your input texts to a shorter length, you can pass the `max_length` parameter to the `encode` function:
+```python
+embeddings = model.encode(["Very long ... document"], max_length=2048)
+
+```
+
+In case you want to use **Matryoshka embeddings** and switch to a different dimension, 
+you can adjust it by passing the `truncate_dim` parameter to the `encode` function:
+```python
+embeddings = model.encode(['Sample text'], truncate_dim=256)
+```
+
+
+The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`:
+
+```bash
+!pip install -U sentence-transformers
+```
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
+
+task = "retrieval.query"
+embeddings = model.encode(
+    ["What is the weather like in Berlin today?"],
+    task=task,
+    prompt_name=task,
+)
+```
+
+You can fine-tune `jina-embeddings-v3` using [SentenceTransformerTrainer](https://sbert.net/docs/package_reference/sentence_transformer/trainer.html). 
+To fine-tune for a specific task, you should set the task before passing the model to the ST Trainer, either during initialization:
+```python
+model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'default_task': 'classification'})
+```
+Or afterwards:
+```python
+model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
+model[0].default_task = 'classification'
+```
+This way you can fine-tune the LoRA adapter for the chosen task.
+
+However, If you want to fine-tune the entire model, make sure the main parameters are set as trainable when loading the model:
+```python
+model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'lora_main_params_trainable': True})
+```
+This will allow fine-tuning the whole model instead of just the LoRA adapters.
+
+
+**<details><summary>ONNX Inference.</summary>**
+<p>
+
+You can use ONNX for efficient inference with `jina-embeddings-v3`:
+```python
+import onnxruntime
+import numpy as np
+from transformers import AutoTokenizer, PretrainedConfig
+
+# Load tokenizer and model config
+tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')
+config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')
+
+# Tokenize input
+input_text = tokenizer('sample text', return_tensors='np')
+
+# ONNX session
+model_path = 'jina-embeddings-v3/onnx/model.onnx'
+session = onnxruntime.InferenceSession(model_path)
+
+# Prepare inputs for ONNX model
+task_type = 'text-matching'
+task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)
+inputs = {
+    'input_ids': input_text['input_ids'],
+    'attention_mask': input_text['attention_mask'],
+    'task_id': task_id
+}
+
+# Run model
+outputs = session.run(None, inputs)[0]
+
+# Apply mean pooling to 'outputs' to get a single representation of each text
+```
+
+</p>
+</details>
+
+
+## Contact
+
+Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
+
+## License
+
+`jina-embeddings-v3` is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to [contact us](https://jina.ai/contact-sales/).
+
+## Citation
+
+If you find `jina-embeddings-v3` useful in your research, please cite the following paper:
+
+```bibtex
+@misc{sturua2024jinaembeddingsv3multilingualembeddingstask,
+      title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA}, 
+      author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao},
+      year={2024},
+      eprint={2409.10173},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.10173}, 
+}
+
+```