diff --git "a/README.md" "b/README.md"
deleted file mode 100644--- "a/README.md"
+++ /dev/null
@@ -1,25307 +0,0 @@
----
-license: cc-by-nc-4.0
-tags:
-- feature-extraction
-- sentence-similarity
-- mteb
-- sentence-transformers
-language:
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- - am
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- - ga
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- - mg
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- - ml
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- - mr
- - ms
- - my
- - ne
- - nl
- - no
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- - or
- - pa
- - pl
- - ps
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- - sd
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- - sk
- - sl
- - so
- - sq
- - sr
- - su
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- - sw
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-inference: false
-library_name: transformers
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-- name: jina-embeddings-v3
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- 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
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- name: MTEB OnlineShopping (default)
- revision: e610f2ebd179a8fda30ae534c3878750a96db120
- split: test
- type: C-MTEB/OnlineShopping-classification
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- type: Classification
- - dataset:
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- name: MTEB OpusparcusPC (en)
- revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
- split: test.full
- type: GEM/opusparcus
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- - type: max_accuracy
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- revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
- split: test.full
- type: GEM/opusparcus
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- - type: manhattan_recall
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- - type: max_accuracy
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- task:
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- revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
- split: test.full
- type: GEM/opusparcus
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- - type: euclidean_accuracy
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- - type: euclidean_ap
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- - type: main_score
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- - type: manhattan_accuracy
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- task:
- type: PairClassification
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- name: MTEB OpusparcusPC (ru)
- revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
- split: test.full
- type: GEM/opusparcus
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- - type: dot_accuracy
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- - type: dot_ap
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- - type: dot_recall
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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: manhattan_accuracy
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- - type: manhattan_accuracy_threshold
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- - type: manhattan_precision
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- - type: manhattan_recall
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- - type: max_accuracy
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- - type: max_ap
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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_precision
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- - type: similarity_recall
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- task:
- type: PairClassification
- - dataset:
- config: default
- name: MTEB PAC (default)
- revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
- split: test
- type: laugustyniak/abusive-clauses-pl
- metrics:
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- - type: ap
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- - type: ap_weighted
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- - type: f1
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- - type: f1_weighted
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- - type: main_score
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- task:
- type: Classification
- - dataset:
- config: default
- name: MTEB PAWSX (default)
- revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
- split: test
- type: C-MTEB/PAWSX
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- - type: cosine_spearman
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- - type: euclidean_pearson
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- - type: euclidean_spearman
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- - type: main_score
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- - type: manhattan_pearson
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- - type: manhattan_spearman
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- - type: pearson
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- - type: spearman
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- task:
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- name: MTEB PSC (default)
- revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
- split: test
- type: PL-MTEB/psc-pairclassification
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- - type: cosine_precision
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- - type: cosine_recall
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- - type: dot_accuracy
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- - type: dot_ap
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- - type: dot_recall
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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: main_score
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- task:
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- name: MTEB PawsXPairClassification (en)
- revision: 8a04d940a42cd40658986fdd8e3da561533a3646
- split: test
- type: google-research-datasets/paws-x
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- - type: dot_accuracy
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- - type: dot_ap
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- - type: dot_recall
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- - type: euclidean_accuracy
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- split: test
- type: google-research-datasets/paws-x
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- type: google-research-datasets/paws-x
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- - type: cosine_accuracy_threshold
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- - type: cosine_f1
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- - type: dot_accuracy
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- - type: euclidean_accuracy
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- - type: main_score
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- - type: manhattan_accuracy
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- - type: manhattan_recall
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- - type: max_accuracy
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- - type: similarity_recall
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- task:
- type: PairClassification
- - dataset:
- config: fr
- name: MTEB PawsXPairClassification (fr)
- revision: 8a04d940a42cd40658986fdd8e3da561533a3646
- split: test
- type: google-research-datasets/paws-x
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- - type: dot_accuracy
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- - type: euclidean_accuracy
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- - type: euclidean_accuracy_threshold
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- - type: euclidean_recall
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- - type: main_score
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- - type: manhattan_accuracy
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- - type: max_accuracy
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- - type: similarity_precision
- value: 45.468277945619334
- - type: similarity_recall
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- task:
- type: PairClassification
- - dataset:
- config: zh
- name: MTEB PawsXPairClassification (zh)
- revision: 8a04d940a42cd40658986fdd8e3da561533a3646
- split: test
- type: google-research-datasets/paws-x
- metrics:
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- value: 100.0
- - type: dot_accuracy
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- - type: euclidean_accuracy
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- - type: main_score
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- - type: max_accuracy
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- - type: max_recall
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- - type: similarity_accuracy
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- task:
- type: PairClassification
- - dataset:
- config: default
- name: MTEB PolEmo2.0-IN (default)
- revision: d90724373c70959f17d2331ad51fb60c71176b03
- split: test
- type: PL-MTEB/polemo2_in
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- - type: main_score
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- task:
- type: Classification
- - dataset:
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- name: MTEB PolEmo2.0-OUT (default)
- revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
- split: test
- type: PL-MTEB/polemo2_out
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- task:
- type: Classification
- - dataset:
- config: default
- name: MTEB PPC
- revision: None
- split: test
- type: PL-MTEB/ppc-pairclassification
- metrics:
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- - type: euclidean_accuracy
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- - type: manhattan_accuracy
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- task:
- type: PairClassification
- - dataset:
- config: default
- name: MTEB QuoraRetrieval (default)
- revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
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- type: mteb/quora
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- - type: recall_at_3
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- - type: recall_at_5
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- task:
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- - dataset:
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- name: MTEB RUParaPhraserSTS (default)
- revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
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- name: MTEB RedditClustering (default)
- revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
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- name: MTEB RiaNewsRetrieval (default)
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- type: ai-forever/ria-news-retrieval
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- 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:
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----
-
-
-
-
-
-
-The embedding model trained by Jina AI. -
- --jina-embeddings-v3: Multilingual Embeddings With Task LoRA -
- -## 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-vm) | [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.** - - -> **⚠️ Important Notice:** -> We fixed a bug in the `encode` function [#60](https://huggingface.co/jinaai/jina-embeddings-v3/discussions/60) where **Matryoshka embedding truncation** occurred *after normalization*, leading to non-normalized truncated embeddings. This issue has been resolved in the latest code revision. -> -> If you have encoded data using the previous version and wish to maintain consistency, please use the specific code revision when loading the model: `AutoModel.from_pretrained('jinaai/jina-embeddings-v3', code_revision='da863dd04a4e5dce6814c6625adfba87b83838aa', ...)` - - -## Usage - -**- -### 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) - -``` - -
-- -You can use ONNX for efficient inference with `jina-embeddings-v3`: -```python -import onnxruntime -import numpy as np -from transformers import AutoTokenizer, PretrainedConfig - -# Mean pool function -def mean_pooling(model_output: np.ndarray, attention_mask: np.ndarray): - token_embeddings = model_output - input_mask_expanded = np.expand_dims(attention_mask, axis=-1) - input_mask_expanded = np.broadcast_to(input_mask_expanded, token_embeddings.shape) - sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1) - sum_mask = np.clip(np.sum(input_mask_expanded, axis=1), a_min=1e-9, a_max=None) - return sum_embeddings / sum_mask - -# 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 and normalization to the model outputs -embeddings = mean_pooling(outputs, input_text["attention_mask"]) -embeddings = embeddings / np.linalg.norm(embeddings, ord=2, axis=1, keepdims=True) -``` - -
-