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- Init (00cb9c04bbf5639a1ebcf9c6a18849008e3dbaee)
- Upload files (748be87653a72bcd943b3b67296565b26d71793e)
- Create gme_inference.py (22270be06dd84a446686b37ab573392089f4c9b0)
- Update gme_inference.py (535408889784412d6f8886c67119190f5b7ab117)
- Update README.md (845c61fa06d42e9796680363dcebc18880c57855)


Co-authored-by: Xin Zhang <izhx@users.noreply.huggingface.co>

README.md

@@ -0,0 +1,3710 @@
+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
+language:
+- en
+- zh
+tags:
+- mteb
+- sentence-transformers
+- transformers
+- Qwen2-VL
+- sentence-similarity
+- vidore
+model-index:
+- name: gme-Qwen2-VL-2B-Instruct
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 72.55223880597015
+    - type: ap
+      value: 35.01515316721116
+    - type: f1
+      value: 66.44086070814382
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 96.75819999999999
+    - type: ap
+      value: 95.51009242092881
+    - type: f1
+      value: 96.75713119357414
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 61.971999999999994
+    - type: f1
+      value: 60.50745575187704
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+    metrics:
+    - type: map_at_1
+      value: 36.272999999999996
+    - type: map_at_10
+      value: 52.782
+    - type: map_at_100
+      value: 53.339999999999996
+    - type: map_at_1000
+      value: 53.342999999999996
+    - type: map_at_3
+      value: 48.4
+    - type: map_at_5
+      value: 50.882000000000005
+    - type: mrr_at_1
+      value: 36.984
+    - type: mrr_at_10
+      value: 53.052
+    - type: mrr_at_100
+      value: 53.604
+    - type: mrr_at_1000
+      value: 53.607000000000006
+    - type: mrr_at_3
+      value: 48.613
+    - type: mrr_at_5
+      value: 51.159
+    - type: ndcg_at_1
+      value: 36.272999999999996
+    - type: ndcg_at_10
+      value: 61.524
+    - type: ndcg_at_100
+      value: 63.796
+    - type: ndcg_at_1000
+      value: 63.869
+    - type: ndcg_at_3
+      value: 52.456
+    - type: ndcg_at_5
+      value: 56.964000000000006
+    - type: precision_at_1
+      value: 36.272999999999996
+    - type: precision_at_10
+      value: 8.926
+    - type: precision_at_100
+      value: 0.989
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 21.407999999999998
+    - type: precision_at_5
+      value: 15.049999999999999
+    - type: recall_at_1
+      value: 36.272999999999996
+    - type: recall_at_10
+      value: 89.25999999999999
+    - type: recall_at_100
+      value: 98.933
+    - type: recall_at_1000
+      value: 99.502
+    - type: recall_at_3
+      value: 64.225
+    - type: recall_at_5
+      value: 75.249
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 52.45236368396085
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 46.83781937870832
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 60.653430349851746
+    - type: mrr
+      value: 74.28736314470387
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 89.18568151905953
+    - type: cos_sim_spearman
+      value: 86.47666922475281
+    - type: euclidean_pearson
+      value: 87.25416218056225
+    - type: euclidean_spearman
+      value: 86.47666922475281
+    - type: manhattan_pearson
+      value: 87.04960508086356
+    - type: manhattan_spearman
+      value: 86.73992823533615
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 80.2435064935065
+    - type: f1
+      value: 79.44078343737895
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 44.68220155432257
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 40.666150477589284
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+    metrics:
+    - type: map_at_1
+      value: 30.623
+    - type: map_at_10
+      value: 40.482
+    - type: map_at_100
+      value: 41.997
+    - type: map_at_1000
+      value: 42.135
+    - type: map_at_3
+      value: 37.754
+    - type: map_at_5
+      value: 39.031
+    - type: mrr_at_1
+      value: 37.482
+    - type: mrr_at_10
+      value: 46.311
+    - type: mrr_at_100
+      value: 47.211999999999996
+    - type: mrr_at_1000
+      value: 47.27
+    - type: mrr_at_3
+      value: 44.157999999999994
+    - type: mrr_at_5
+      value: 45.145
+    - type: ndcg_at_1
+      value: 37.482
+    - type: ndcg_at_10
+      value: 46.142
+    - type: ndcg_at_100
+      value: 51.834
+    - type: ndcg_at_1000
+      value: 54.164
+    - type: ndcg_at_3
+      value: 42.309000000000005
+    - type: ndcg_at_5
+      value: 43.485
+    - type: precision_at_1
+      value: 37.482
+    - type: precision_at_10
+      value: 8.455
+    - type: precision_at_100
+      value: 1.3780000000000001
+    - type: precision_at_1000
+      value: 0.188
+    - type: precision_at_3
+      value: 20.172
+    - type: precision_at_5
+      value: 13.705
+    - type: recall_at_1
+      value: 30.623
+    - type: recall_at_10
+      value: 56.77100000000001
+    - type: recall_at_100
+      value: 80.034
+    - type: recall_at_1000
+      value: 94.62899999999999
+    - type: recall_at_3
+      value: 44.663000000000004
+    - type: recall_at_5
+      value: 48.692
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+    metrics:
+    - type: map_at_1
+      value: 27.941
+    - type: map_at_10
+      value: 38.437
+    - type: map_at_100
+      value: 39.625
+    - type: map_at_1000
+      value: 39.753
+    - type: map_at_3
+      value: 35.388999999999996
+    - type: map_at_5
+      value: 37.113
+    - type: mrr_at_1
+      value: 34.522000000000006
+    - type: mrr_at_10
+      value: 43.864999999999995
+    - type: mrr_at_100
+      value: 44.533
+    - type: mrr_at_1000
+      value: 44.580999999999996
+    - type: mrr_at_3
+      value: 41.55
+    - type: mrr_at_5
+      value: 42.942
+    - type: ndcg_at_1
+      value: 34.522000000000006
+    - type: ndcg_at_10
+      value: 44.330000000000005
+    - type: ndcg_at_100
+      value: 48.61
+    - type: ndcg_at_1000
+      value: 50.712999999999994
+    - type: ndcg_at_3
+      value: 39.834
+    - type: ndcg_at_5
+      value: 42.016
+    - type: precision_at_1
+      value: 34.522000000000006
+    - type: precision_at_10
+      value: 8.471
+    - type: precision_at_100
+      value: 1.3379999999999999
+    - type: precision_at_1000
+      value: 0.182
+    - type: precision_at_3
+      value: 19.363
+    - type: precision_at_5
+      value: 13.898
+    - type: recall_at_1
+      value: 27.941
+    - type: recall_at_10
+      value: 55.336
+    - type: recall_at_100
+      value: 73.51100000000001
+    - type: recall_at_1000
+      value: 86.636
+    - type: recall_at_3
+      value: 42.54
+    - type: recall_at_5
+      value: 48.392
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+    metrics:
+    - type: map_at_1
+      value: 32.681
+    - type: map_at_10
+      value: 45.48
+    - type: map_at_100
+      value: 46.542
+    - type: map_at_1000
+      value: 46.604
+    - type: map_at_3
+      value: 42.076
+    - type: map_at_5
+      value: 44.076
+    - type: mrr_at_1
+      value: 37.492
+    - type: mrr_at_10
+      value: 48.746
+    - type: mrr_at_100
+      value: 49.485
+    - type: mrr_at_1000
+      value: 49.517
+    - type: mrr_at_3
+      value: 45.998
+    - type: mrr_at_5
+      value: 47.681000000000004
+    - type: ndcg_at_1
+      value: 37.492
+    - type: ndcg_at_10
+      value: 51.778999999999996
+    - type: ndcg_at_100
+      value: 56.294
+    - type: ndcg_at_1000
+      value: 57.58
+    - type: ndcg_at_3
+      value: 45.856
+    - type: ndcg_at_5
+      value: 48.968
+    - type: precision_at_1
+      value: 37.492
+    - type: precision_at_10
+      value: 8.620999999999999
+    - type: precision_at_100
+      value: 1.189
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 20.773
+    - type: precision_at_5
+      value: 14.596
+    - type: recall_at_1
+      value: 32.681
+    - type: recall_at_10
+      value: 67.196
+    - type: recall_at_100
+      value: 87.027
+    - type: recall_at_1000
+      value: 96.146
+    - type: recall_at_3
+      value: 51.565000000000005
+    - type: recall_at_5
+      value: 59.123999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+    metrics:
+    - type: map_at_1
+      value: 22.421
+    - type: map_at_10
+      value: 30.127
+    - type: map_at_100
+      value: 31.253999999999998
+    - type: map_at_1000
+      value: 31.344
+    - type: map_at_3
+      value: 27.673
+    - type: map_at_5
+      value: 29.182000000000002
+    - type: mrr_at_1
+      value: 24.068
+    - type: mrr_at_10
+      value: 31.857000000000003
+    - type: mrr_at_100
+      value: 32.808
+    - type: mrr_at_1000
+      value: 32.881
+    - type: mrr_at_3
+      value: 29.397000000000002
+    - type: mrr_at_5
+      value: 30.883
+    - type: ndcg_at_1
+      value: 24.068
+    - type: ndcg_at_10
+      value: 34.642
+    - type: ndcg_at_100
+      value: 40.327
+    - type: ndcg_at_1000
+      value: 42.55
+    - type: ndcg_at_3
+      value: 29.868
+    - type: ndcg_at_5
+      value: 32.461
+    - type: precision_at_1
+      value: 24.068
+    - type: precision_at_10
+      value: 5.390000000000001
+    - type: precision_at_100
+      value: 0.873
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 12.692999999999998
+    - type: precision_at_5
+      value: 9.107
+    - type: recall_at_1
+      value: 22.421
+    - type: recall_at_10
+      value: 46.846
+    - type: recall_at_100
+      value: 73.409
+    - type: recall_at_1000
+      value: 90.06
+    - type: recall_at_3
+      value: 34.198
+    - type: recall_at_5
+      value: 40.437
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+    metrics:
+    - type: map_at_1
+      value: 16.494
+    - type: map_at_10
+      value: 24.4
+    - type: map_at_100
+      value: 25.718999999999998
+    - type: map_at_1000
+      value: 25.840000000000003
+    - type: map_at_3
+      value: 21.731
+    - type: map_at_5
+      value: 23.247999999999998
+    - type: mrr_at_1
+      value: 20.274
+    - type: mrr_at_10
+      value: 28.866000000000003
+    - type: mrr_at_100
+      value: 29.889
+    - type: mrr_at_1000
+      value: 29.957
+    - type: mrr_at_3
+      value: 26.284999999999997
+    - type: mrr_at_5
+      value: 27.79
+    - type: ndcg_at_1
+      value: 20.274
+    - type: ndcg_at_10
+      value: 29.666999999999998
+    - type: ndcg_at_100
+      value: 36.095
+    - type: ndcg_at_1000
+      value: 38.87
+    - type: ndcg_at_3
+      value: 24.672
+    - type: ndcg_at_5
+      value: 27.106
+    - type: precision_at_1
+      value: 20.274
+    - type: precision_at_10
+      value: 5.5969999999999995
+    - type: precision_at_100
+      value: 1.04
+    - type: precision_at_1000
+      value: 0.14100000000000001
+    - type: precision_at_3
+      value: 12.023
+    - type: precision_at_5
+      value: 8.98
+    - type: recall_at_1
+      value: 16.494
+    - type: recall_at_10
+      value: 41.400999999999996
+    - type: recall_at_100
+      value: 69.811
+    - type: recall_at_1000
+      value: 89.422
+    - type: recall_at_3
+      value: 27.834999999999997
+    - type: recall_at_5
+      value: 33.774
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+    metrics:
+    - type: map_at_1
+      value: 26.150000000000002
+    - type: map_at_10
+      value: 36.012
+    - type: map_at_100
+      value: 37.377
+    - type: map_at_1000
+      value: 37.497
+    - type: map_at_3
+      value: 32.712
+    - type: map_at_5
+      value: 34.475
+    - type: mrr_at_1
+      value: 32.05
+    - type: mrr_at_10
+      value: 41.556
+    - type: mrr_at_100
+      value: 42.451
+    - type: mrr_at_1000
+      value: 42.498000000000005
+    - type: mrr_at_3
+      value: 38.659
+    - type: mrr_at_5
+      value: 40.314
+    - type: ndcg_at_1
+      value: 32.05
+    - type: ndcg_at_10
+      value: 42.132
+    - type: ndcg_at_100
+      value: 48.028999999999996
+    - type: ndcg_at_1000
+      value: 50.229
+    - type: ndcg_at_3
+      value: 36.622
+    - type: ndcg_at_5
+      value: 39.062000000000005
+    - type: precision_at_1
+      value: 32.05
+    - type: precision_at_10
+      value: 7.767
+    - type: precision_at_100
+      value: 1.269
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_3
+      value: 17.355999999999998
+    - type: precision_at_5
+      value: 12.474
+    - type: recall_at_1
+      value: 26.150000000000002
+    - type: recall_at_10
+      value: 55.205000000000005
+    - type: recall_at_100
+      value: 80.2
+    - type: recall_at_1000
+      value: 94.524
+    - type: recall_at_3
+      value: 39.322
+    - type: recall_at_5
+      value: 45.761
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+    metrics:
+    - type: map_at_1
+      value: 23.741
+    - type: map_at_10
+      value: 33.51
+    - type: map_at_100
+      value: 34.882999999999996
+    - type: map_at_1000
+      value: 34.995
+    - type: map_at_3
+      value: 30.514000000000003
+    - type: map_at_5
+      value: 32.085
+    - type: mrr_at_1
+      value: 28.653000000000002
+    - type: mrr_at_10
+      value: 38.059
+    - type: mrr_at_100
+      value: 39.050000000000004
+    - type: mrr_at_1000
+      value: 39.107
+    - type: mrr_at_3
+      value: 35.445
+    - type: mrr_at_5
+      value: 36.849
+    - type: ndcg_at_1
+      value: 28.653000000000002
+    - type: ndcg_at_10
+      value: 39.186
+    - type: ndcg_at_100
+      value: 45.301
+    - type: ndcg_at_1000
+      value: 47.547
+    - type: ndcg_at_3
+      value: 34.103
+    - type: ndcg_at_5
+      value: 36.239
+    - type: precision_at_1
+      value: 28.653000000000002
+    - type: precision_at_10
+      value: 7.295
+    - type: precision_at_100
+      value: 1.2189999999999999
+    - type: precision_at_1000
+      value: 0.159
+    - type: precision_at_3
+      value: 16.438
+    - type: precision_at_5
+      value: 11.804
+    - type: recall_at_1
+      value: 23.741
+    - type: recall_at_10
+      value: 51.675000000000004
+    - type: recall_at_100
+      value: 78.13799999999999
+    - type: recall_at_1000
+      value: 93.12700000000001
+    - type: recall_at_3
+      value: 37.033
+    - type: recall_at_5
+      value: 42.793
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+    metrics:
+    - type: map_at_1
+      value: 23.452
+    - type: map_at_10
+      value: 30.231
+    - type: map_at_100
+      value: 31.227
+    - type: map_at_1000
+      value: 31.338
+    - type: map_at_3
+      value: 28.083000000000002
+    - type: map_at_5
+      value: 29.125
+    - type: mrr_at_1
+      value: 25.613000000000003
+    - type: mrr_at_10
+      value: 32.62
+    - type: mrr_at_100
+      value: 33.469
+    - type: mrr_at_1000
+      value: 33.554
+    - type: mrr_at_3
+      value: 30.368000000000002
+    - type: mrr_at_5
+      value: 31.502999999999997
+    - type: ndcg_at_1
+      value: 25.613000000000003
+    - type: ndcg_at_10
+      value: 34.441
+    - type: ndcg_at_100
+      value: 39.253
+    - type: ndcg_at_1000
+      value: 42.105
+    - type: ndcg_at_3
+      value: 30.183
+    - type: ndcg_at_5
+      value: 31.917
+    - type: precision_at_1
+      value: 25.613000000000003
+    - type: precision_at_10
+      value: 5.367999999999999
+    - type: precision_at_100
+      value: 0.848
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 12.73
+    - type: precision_at_5
+      value: 8.773
+    - type: recall_at_1
+      value: 23.452
+    - type: recall_at_10
+      value: 45.021
+    - type: recall_at_100
+      value: 66.563
+    - type: recall_at_1000
+      value: 87.713
+    - type: recall_at_3
+      value: 33.433
+    - type: recall_at_5
+      value: 37.637
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+    metrics:
+    - type: map_at_1
+      value: 16.11
+    - type: map_at_10
+      value: 22.832
+    - type: map_at_100
+      value: 23.829
+    - type: map_at_1000
+      value: 23.959
+    - type: map_at_3
+      value: 20.66
+    - type: map_at_5
+      value: 21.851000000000003
+    - type: mrr_at_1
+      value: 19.408
+    - type: mrr_at_10
+      value: 26.354
+    - type: mrr_at_100
+      value: 27.237000000000002
+    - type: mrr_at_1000
+      value: 27.32
+    - type: mrr_at_3
+      value: 24.243000000000002
+    - type: mrr_at_5
+      value: 25.430000000000003
+    - type: ndcg_at_1
+      value: 19.408
+    - type: ndcg_at_10
+      value: 27.239
+    - type: ndcg_at_100
+      value: 32.286
+    - type: ndcg_at_1000
+      value: 35.498000000000005
+    - type: ndcg_at_3
+      value: 23.244
+    - type: ndcg_at_5
+      value: 25.080999999999996
+    - type: precision_at_1
+      value: 19.408
+    - type: precision_at_10
+      value: 4.917
+    - type: precision_at_100
+      value: 0.874
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 10.863
+    - type: precision_at_5
+      value: 7.887
+    - type: recall_at_1
+      value: 16.11
+    - type: recall_at_10
+      value: 37.075
+    - type: recall_at_100
+      value: 60.251999999999995
+    - type: recall_at_1000
+      value: 83.38600000000001
+    - type: recall_at_3
+      value: 25.901999999999997
+    - type: recall_at_5
+      value: 30.612000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+    metrics:
+    - type: map_at_1
+      value: 25.941
+    - type: map_at_10
+      value: 33.711999999999996
+    - type: map_at_100
+      value: 34.926
+    - type: map_at_1000
+      value: 35.05
+    - type: map_at_3
+      value: 31.075000000000003
+    - type: map_at_5
+      value: 32.611000000000004
+    - type: mrr_at_1
+      value: 30.784
+    - type: mrr_at_10
+      value: 38.079
+    - type: mrr_at_100
+      value: 39.018
+    - type: mrr_at_1000
+      value: 39.09
+    - type: mrr_at_3
+      value: 35.603
+    - type: mrr_at_5
+      value: 36.988
+    - type: ndcg_at_1
+      value: 30.784
+    - type: ndcg_at_10
+      value: 38.586
+    - type: ndcg_at_100
+      value: 44.205
+    - type: ndcg_at_1000
+      value: 46.916000000000004
+    - type: ndcg_at_3
+      value: 33.899
+    - type: ndcg_at_5
+      value: 36.11
+    - type: precision_at_1
+      value: 30.784
+    - type: precision_at_10
+      value: 6.409
+    - type: precision_at_100
+      value: 1.034
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 15.112
+    - type: precision_at_5
+      value: 10.728
+    - type: recall_at_1
+      value: 25.941
+    - type: recall_at_10
+      value: 49.242999999999995
+    - type: recall_at_100
+      value: 73.85000000000001
+    - type: recall_at_1000
+      value: 92.782
+    - type: recall_at_3
+      value: 36.204
+    - type: recall_at_5
+      value: 41.908
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+    metrics:
+    - type: map_at_1
+      value: 24.401999999999997
+    - type: map_at_10
+      value: 33.195
+    - type: map_at_100
+      value: 34.699999999999996
+    - type: map_at_1000
+      value: 34.946
+    - type: map_at_3
+      value: 30.570999999999998
+    - type: map_at_5
+      value: 32.0
+    - type: mrr_at_1
+      value: 28.656
+    - type: mrr_at_10
+      value: 37.039
+    - type: mrr_at_100
+      value: 38.049
+    - type: mrr_at_1000
+      value: 38.108
+    - type: mrr_at_3
+      value: 34.717
+    - type: mrr_at_5
+      value: 36.07
+    - type: ndcg_at_1
+      value: 28.656
+    - type: ndcg_at_10
+      value: 38.557
+    - type: ndcg_at_100
+      value: 44.511
+    - type: ndcg_at_1000
+      value: 47.346
+    - type: ndcg_at_3
+      value: 34.235
+    - type: ndcg_at_5
+      value: 36.260999999999996
+    - type: precision_at_1
+      value: 28.656
+    - type: precision_at_10
+      value: 7.312
+    - type: precision_at_100
+      value: 1.451
+    - type: precision_at_1000
+      value: 0.242
+    - type: precision_at_3
+      value: 15.942
+    - type: precision_at_5
+      value: 11.66
+    - type: recall_at_1
+      value: 24.401999999999997
+    - type: recall_at_10
+      value: 48.791000000000004
+    - type: recall_at_100
+      value: 76.211
+    - type: recall_at_1000
+      value: 93.92
+    - type: recall_at_3
+      value: 36.975
+    - type: recall_at_5
+      value: 42.01
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: map_at_1
+      value: 19.07
+    - type: map_at_10
+      value: 26.608999999999998
+    - type: map_at_100
+      value: 27.625
+    - type: map_at_1000
+      value: 27.743000000000002
+    - type: map_at_3
+      value: 24.532999999999998
+    - type: map_at_5
+      value: 25.671
+    - type: mrr_at_1
+      value: 20.518
+    - type: mrr_at_10
+      value: 28.541
+    - type: mrr_at_100
+      value: 29.453000000000003
+    - type: mrr_at_1000
+      value: 29.536
+    - type: mrr_at_3
+      value: 26.71
+    - type: mrr_at_5
+      value: 27.708
+    - type: ndcg_at_1
+      value: 20.518
+    - type: ndcg_at_10
+      value: 30.855
+    - type: ndcg_at_100
+      value: 35.973
+    - type: ndcg_at_1000
+      value: 38.827
+    - type: ndcg_at_3
+      value: 26.868
+    - type: ndcg_at_5
+      value: 28.74
+    - type: precision_at_1
+      value: 20.518
+    - type: precision_at_10
+      value: 4.843
+    - type: precision_at_100
+      value: 0.799
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 11.645
+    - type: precision_at_5
+      value: 8.133
+    - type: recall_at_1
+      value: 19.07
+    - type: recall_at_10
+      value: 41.925000000000004
+    - type: recall_at_100
+      value: 65.68
+    - type: recall_at_1000
+      value: 86.713
+    - type: recall_at_3
+      value: 31.251
+    - type: recall_at_5
+      value: 35.653
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+    metrics:
+    - type: map_at_1
+      value: 18.762
+    - type: map_at_10
+      value: 32.412
+    - type: map_at_100
+      value: 34.506
+    - type: map_at_1000
+      value: 34.678
+    - type: map_at_3
+      value: 27.594
+    - type: map_at_5
+      value: 30.128
+    - type: mrr_at_1
+      value: 42.345
+    - type: mrr_at_10
+      value: 54.443
+    - type: mrr_at_100
+      value: 55.05799999999999
+    - type: mrr_at_1000
+      value: 55.076
+    - type: mrr_at_3
+      value: 51.553000000000004
+    - type: mrr_at_5
+      value: 53.269
+    - type: ndcg_at_1
+      value: 42.345
+    - type: ndcg_at_10
+      value: 42.304
+    - type: ndcg_at_100
+      value: 49.425000000000004
+    - type: ndcg_at_1000
+      value: 52.123
+    - type: ndcg_at_3
+      value: 36.271
+    - type: ndcg_at_5
+      value: 38.216
+    - type: precision_at_1
+      value: 42.345
+    - type: precision_at_10
+      value: 12.808
+    - type: precision_at_100
+      value: 2.062
+    - type: precision_at_1000
+      value: 0.258
+    - type: precision_at_3
+      value: 26.840000000000003
+    - type: precision_at_5
+      value: 20.052
+    - type: recall_at_1
+      value: 18.762
+    - type: recall_at_10
+      value: 47.976
+    - type: recall_at_100
+      value: 71.86
+    - type: recall_at_1000
+      value: 86.61999999999999
+    - type: recall_at_3
+      value: 32.708999999999996
+    - type: recall_at_5
+      value: 39.151
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/dbpedia
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+    metrics:
+    - type: map_at_1
+      value: 9.685
+    - type: map_at_10
+      value: 21.65
+    - type: map_at_100
+      value: 30.952
+    - type: map_at_1000
+      value: 33.049
+    - type: map_at_3
+      value: 14.953
+    - type: map_at_5
+      value: 17.592
+    - type: mrr_at_1
+      value: 72.0
+    - type: mrr_at_10
+      value: 78.054
+    - type: mrr_at_100
+      value: 78.41900000000001
+    - type: mrr_at_1000
+      value: 78.425
+    - type: mrr_at_3
+      value: 76.5
+    - type: mrr_at_5
+      value: 77.28699999999999
+    - type: ndcg_at_1
+      value: 61.25000000000001
+    - type: ndcg_at_10
+      value: 46.306000000000004
+    - type: ndcg_at_100
+      value: 50.867
+    - type: ndcg_at_1000
+      value: 58.533
+    - type: ndcg_at_3
+      value: 50.857
+    - type: ndcg_at_5
+      value: 48.283
+    - type: precision_at_1
+      value: 72.0
+    - type: precision_at_10
+      value: 37.3
+    - type: precision_at_100
+      value: 11.95
+    - type: precision_at_1000
+      value: 2.528
+    - type: precision_at_3
+      value: 53.583000000000006
+    - type: precision_at_5
+      value: 46.6
+    - type: recall_at_1
+      value: 9.685
+    - type: recall_at_10
+      value: 27.474999999999998
+    - type: recall_at_100
+      value: 56.825
+    - type: recall_at_1000
+      value: 81.792
+    - type: recall_at_3
+      value: 15.939
+    - type: recall_at_5
+      value: 19.853
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 62.805000000000014
+    - type: f1
+      value: 56.401757250989384
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+    metrics:
+    - type: map_at_1
+      value: 83.734
+    - type: map_at_10
+      value: 90.089
+    - type: map_at_100
+      value: 90.274
+    - type: map_at_1000
+      value: 90.286
+    - type: map_at_3
+      value: 89.281
+    - type: map_at_5
+      value: 89.774
+    - type: mrr_at_1
+      value: 90.039
+    - type: mrr_at_10
+      value: 94.218
+    - type: mrr_at_100
+      value: 94.24
+    - type: mrr_at_1000
+      value: 94.24
+    - type: mrr_at_3
+      value: 93.979
+    - type: mrr_at_5
+      value: 94.137
+    - type: ndcg_at_1
+      value: 90.039
+    - type: ndcg_at_10
+      value: 92.597
+    - type: ndcg_at_100
+      value: 93.147
+    - type: ndcg_at_1000
+      value: 93.325
+    - type: ndcg_at_3
+      value: 91.64999999999999
+    - type: ndcg_at_5
+      value: 92.137
+    - type: precision_at_1
+      value: 90.039
+    - type: precision_at_10
+      value: 10.809000000000001
+    - type: precision_at_100
+      value: 1.133
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 34.338
+    - type: precision_at_5
+      value: 21.089
+    - type: recall_at_1
+      value: 83.734
+    - type: recall_at_10
+      value: 96.161
+    - type: recall_at_100
+      value: 98.137
+    - type: recall_at_1000
+      value: 99.182
+    - type: recall_at_3
+      value: 93.551
+    - type: recall_at_5
+      value: 94.878
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+    metrics:
+    - type: map_at_1
+      value: 24.529999999999998
+    - type: map_at_10
+      value: 37.229
+    - type: map_at_100
+      value: 39.333
+    - type: map_at_1000
+      value: 39.491
+    - type: map_at_3
+      value: 32.177
+    - type: map_at_5
+      value: 35.077999999999996
+    - type: mrr_at_1
+      value: 45.678999999999995
+    - type: mrr_at_10
+      value: 53.952
+    - type: mrr_at_100
+      value: 54.727000000000004
+    - type: mrr_at_1000
+      value: 54.761
+    - type: mrr_at_3
+      value: 51.568999999999996
+    - type: mrr_at_5
+      value: 52.973000000000006
+    - type: ndcg_at_1
+      value: 45.678999999999995
+    - type: ndcg_at_10
+      value: 45.297
+    - type: ndcg_at_100
+      value: 52.516
+    - type: ndcg_at_1000
+      value: 55.16
+    - type: ndcg_at_3
+      value: 40.569
+    - type: ndcg_at_5
+      value: 42.49
+    - type: precision_at_1
+      value: 45.678999999999995
+    - type: precision_at_10
+      value: 12.269
+    - type: precision_at_100
+      value: 1.9709999999999999
+    - type: precision_at_1000
+      value: 0.244
+    - type: precision_at_3
+      value: 25.72
+    - type: precision_at_5
+      value: 19.66
+    - type: recall_at_1
+      value: 24.529999999999998
+    - type: recall_at_10
+      value: 51.983999999999995
+    - type: recall_at_100
+      value: 78.217
+    - type: recall_at_1000
+      value: 94.104
+    - type: recall_at_3
+      value: 36.449999999999996
+    - type: recall_at_5
+      value: 43.336999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+    metrics:
+    - type: map_at_1
+      value: 41.519
+    - type: map_at_10
+      value: 64.705
+    - type: map_at_100
+      value: 65.554
+    - type: map_at_1000
+      value: 65.613
+    - type: map_at_3
+      value: 61.478
+    - type: map_at_5
+      value: 63.55800000000001
+    - type: mrr_at_1
+      value: 83.038
+    - type: mrr_at_10
+      value: 87.82900000000001
+    - type: mrr_at_100
+      value: 87.96000000000001
+    - type: mrr_at_1000
+      value: 87.96300000000001
+    - type: mrr_at_3
+      value: 87.047
+    - type: mrr_at_5
+      value: 87.546
+    - type: ndcg_at_1
+      value: 83.038
+    - type: ndcg_at_10
+      value: 72.928
+    - type: ndcg_at_100
+      value: 75.778
+    - type: ndcg_at_1000
+      value: 76.866
+    - type: ndcg_at_3
+      value: 68.46600000000001
+    - type: ndcg_at_5
+      value: 71.036
+    - type: precision_at_1
+      value: 83.038
+    - type: precision_at_10
+      value: 15.040999999999999
+    - type: precision_at_100
+      value: 1.7260000000000002
+    - type: precision_at_1000
+      value: 0.187
+    - type: precision_at_3
+      value: 43.597
+    - type: precision_at_5
+      value: 28.188999999999997
+    - type: recall_at_1
+      value: 41.519
+    - type: recall_at_10
+      value: 75.20599999999999
+    - type: recall_at_100
+      value: 86.3
+    - type: recall_at_1000
+      value: 93.437
+    - type: recall_at_3
+      value: 65.39500000000001
+    - type: recall_at_5
+      value: 70.473
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 96.0428
+    - type: ap
+      value: 94.48278082595033
+    - type: f1
+      value: 96.0409595432081
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+    metrics:
+    - type: map_at_1
+      value: 21.496000000000002
+    - type: map_at_10
+      value: 33.82
+    - type: map_at_100
+      value: 35.013
+    - type: map_at_1000
+      value: 35.063
+    - type: map_at_3
+      value: 29.910999999999998
+    - type: map_at_5
+      value: 32.086
+    - type: mrr_at_1
+      value: 22.092
+    - type: mrr_at_10
+      value: 34.404
+    - type: mrr_at_100
+      value: 35.534
+    - type: mrr_at_1000
+      value: 35.577999999999996
+    - type: mrr_at_3
+      value: 30.544
+    - type: mrr_at_5
+      value: 32.711
+    - type: ndcg_at_1
+      value: 22.092
+    - type: ndcg_at_10
+      value: 40.877
+    - type: ndcg_at_100
+      value: 46.619
+    - type: ndcg_at_1000
+      value: 47.823
+    - type: ndcg_at_3
+      value: 32.861000000000004
+    - type: ndcg_at_5
+      value: 36.769
+    - type: precision_at_1
+      value: 22.092
+    - type: precision_at_10
+      value: 6.54
+    - type: precision_at_100
+      value: 0.943
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 14.069
+    - type: precision_at_5
+      value: 10.424
+    - type: recall_at_1
+      value: 21.496000000000002
+    - type: recall_at_10
+      value: 62.67
+    - type: recall_at_100
+      value: 89.24499999999999
+    - type: recall_at_1000
+      value: 98.312
+    - type: recall_at_3
+      value: 40.796
+    - type: recall_at_5
+      value: 50.21600000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 95.74555403556772
+    - type: f1
+      value: 95.61381879323093
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 85.82763337893297
+    - type: f1
+      value: 63.17139719465236
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 78.51714862138535
+    - type: f1
+      value: 76.3995118440293
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 80.03698722259583
+    - type: f1
+      value: 79.36511484240766
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 38.68901889835701
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 38.0740589898848
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 33.41312482460189
+    - type: mrr
+      value: 34.713530863302495
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+    metrics:
+    - type: map_at_1
+      value: 6.232
+    - type: map_at_10
+      value: 13.442000000000002
+    - type: map_at_100
+      value: 17.443
+    - type: map_at_1000
+      value: 19.1
+    - type: map_at_3
+      value: 9.794
+    - type: map_at_5
+      value: 11.375
+    - type: mrr_at_1
+      value: 50.15500000000001
+    - type: mrr_at_10
+      value: 58.628
+    - type: mrr_at_100
+      value: 59.077
+    - type: mrr_at_1000
+      value: 59.119
+    - type: mrr_at_3
+      value: 56.914
+    - type: mrr_at_5
+      value: 57.921
+    - type: ndcg_at_1
+      value: 48.762
+    - type: ndcg_at_10
+      value: 37.203
+    - type: ndcg_at_100
+      value: 34.556
+    - type: ndcg_at_1000
+      value: 43.601
+    - type: ndcg_at_3
+      value: 43.004
+    - type: ndcg_at_5
+      value: 40.181
+    - type: precision_at_1
+      value: 50.15500000000001
+    - type: precision_at_10
+      value: 27.276
+    - type: precision_at_100
+      value: 8.981
+    - type: precision_at_1000
+      value: 2.228
+    - type: precision_at_3
+      value: 39.628
+    - type: precision_at_5
+      value: 33.808
+    - type: recall_at_1
+      value: 6.232
+    - type: recall_at_10
+      value: 18.137
+    - type: recall_at_100
+      value: 36.101
+    - type: recall_at_1000
+      value: 68.733
+    - type: recall_at_3
+      value: 10.978
+    - type: recall_at_5
+      value: 13.718
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+    metrics:
+    - type: map_at_1
+      value: 35.545
+    - type: map_at_10
+      value: 52.083
+    - type: map_at_100
+      value: 52.954
+    - type: map_at_1000
+      value: 52.96999999999999
+    - type: map_at_3
+      value: 47.508
+    - type: map_at_5
+      value: 50.265
+    - type: mrr_at_1
+      value: 40.122
+    - type: mrr_at_10
+      value: 54.567
+    - type: mrr_at_100
+      value: 55.19199999999999
+    - type: mrr_at_1000
+      value: 55.204
+    - type: mrr_at_3
+      value: 51.043000000000006
+    - type: mrr_at_5
+      value: 53.233
+    - type: ndcg_at_1
+      value: 40.122
+    - type: ndcg_at_10
+      value: 60.012
+    - type: ndcg_at_100
+      value: 63.562
+    - type: ndcg_at_1000
+      value: 63.94
+    - type: ndcg_at_3
+      value: 51.681
+    - type: ndcg_at_5
+      value: 56.154
+    - type: precision_at_1
+      value: 40.122
+    - type: precision_at_10
+      value: 9.774
+    - type: precision_at_100
+      value: 1.176
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 23.426
+    - type: precision_at_5
+      value: 16.686
+    - type: recall_at_1
+      value: 35.545
+    - type: recall_at_10
+      value: 81.557
+    - type: recall_at_100
+      value: 96.729
+    - type: recall_at_1000
+      value: 99.541
+    - type: recall_at_3
+      value: 60.185
+    - type: recall_at_5
+      value: 70.411
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 68.908
+    - type: map_at_10
+      value: 83.19
+    - type: map_at_100
+      value: 83.842
+    - type: map_at_1000
+      value: 83.858
+    - type: map_at_3
+      value: 80.167
+    - type: map_at_5
+      value: 82.053
+    - type: mrr_at_1
+      value: 79.46
+    - type: mrr_at_10
+      value: 86.256
+    - type: mrr_at_100
+      value: 86.37
+    - type: mrr_at_1000
+      value: 86.371
+    - type: mrr_at_3
+      value: 85.177
+    - type: mrr_at_5
+      value: 85.908
+    - type: ndcg_at_1
+      value: 79.5
+    - type: ndcg_at_10
+      value: 87.244
+    - type: ndcg_at_100
+      value: 88.532
+    - type: ndcg_at_1000
+      value: 88.626
+    - type: ndcg_at_3
+      value: 84.161
+    - type: ndcg_at_5
+      value: 85.835
+    - type: precision_at_1
+      value: 79.5
+    - type: precision_at_10
+      value: 13.339
+    - type: precision_at_100
+      value: 1.53
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 36.97
+    - type: precision_at_5
+      value: 24.384
+    - type: recall_at_1
+      value: 68.908
+    - type: recall_at_10
+      value: 95.179
+    - type: recall_at_100
+      value: 99.579
+    - type: recall_at_1000
+      value: 99.964
+    - type: recall_at_3
+      value: 86.424
+    - type: recall_at_5
+      value: 91.065
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 65.17897847862794
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 66.22194961632586
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.668
+    - type: map_at_10
+      value: 13.921
+    - type: map_at_100
+      value: 16.391
+    - type: map_at_1000
+      value: 16.749
+    - type: map_at_3
+      value: 10.001999999999999
+    - type: map_at_5
+      value: 11.974
+    - type: mrr_at_1
+      value: 27.800000000000004
+    - type: mrr_at_10
+      value: 39.290000000000006
+    - type: mrr_at_100
+      value: 40.313
+    - type: mrr_at_1000
+      value: 40.355999999999995
+    - type: mrr_at_3
+      value: 35.667
+    - type: mrr_at_5
+      value: 37.742
+    - type: ndcg_at_1
+      value: 27.800000000000004
+    - type: ndcg_at_10
+      value: 23.172
+    - type: ndcg_at_100
+      value: 32.307
+    - type: ndcg_at_1000
+      value: 38.048
+    - type: ndcg_at_3
+      value: 22.043
+    - type: ndcg_at_5
+      value: 19.287000000000003
+    - type: precision_at_1
+      value: 27.800000000000004
+    - type: precision_at_10
+      value: 11.95
+    - type: precision_at_100
+      value: 2.5260000000000002
+    - type: precision_at_1000
+      value: 0.38999999999999996
+    - type: precision_at_3
+      value: 20.433
+    - type: precision_at_5
+      value: 16.84
+    - type: recall_at_1
+      value: 5.668
+    - type: recall_at_10
+      value: 24.22
+    - type: recall_at_100
+      value: 51.217
+    - type: recall_at_1000
+      value: 79.10000000000001
+    - type: recall_at_3
+      value: 12.443
+    - type: recall_at_5
+      value: 17.068
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.83535239748218
+    - type: cos_sim_spearman
+      value: 73.98553311584509
+    - type: euclidean_pearson
+      value: 79.57336200069007
+    - type: euclidean_spearman
+      value: 73.98553926018461
+    - type: manhattan_pearson
+      value: 79.02277757114132
+    - type: manhattan_spearman
+      value: 73.52350678760683
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.99055838690317
+    - type: cos_sim_spearman
+      value: 72.05290668592296
+    - type: euclidean_pearson
+      value: 81.7130610313565
+    - type: euclidean_spearman
+      value: 72.0529066787229
+    - type: manhattan_pearson
+      value: 82.09213883730894
+    - type: manhattan_spearman
+      value: 72.5171577483134
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.4685161191763
+    - type: cos_sim_spearman
+      value: 84.4847436140129
+    - type: euclidean_pearson
+      value: 84.05016757016948
+    - type: euclidean_spearman
+      value: 84.48474353891532
+    - type: manhattan_pearson
+      value: 83.83064062713048
+    - type: manhattan_spearman
+      value: 84.30431591842805
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.00171021092486
+    - type: cos_sim_spearman
+      value: 77.91329577609622
+    - type: euclidean_pearson
+      value: 81.49758593915315
+    - type: euclidean_spearman
+      value: 77.91329577609622
+    - type: manhattan_pearson
+      value: 81.23255996803785
+    - type: manhattan_spearman
+      value: 77.80027024941825
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.62608607472492
+    - type: cos_sim_spearman
+      value: 87.62293916855751
+    - type: euclidean_pearson
+      value: 87.04313886714989
+    - type: euclidean_spearman
+      value: 87.62293907119869
+    - type: manhattan_pearson
+      value: 86.97266321040769
+    - type: manhattan_spearman
+      value: 87.61807042381702
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.8012095789289
+    - type: cos_sim_spearman
+      value: 81.91868918081325
+    - type: euclidean_pearson
+      value: 81.2267973811213
+    - type: euclidean_spearman
+      value: 81.91868918081325
+    - type: manhattan_pearson
+      value: 81.0173457901168
+    - type: manhattan_spearman
+      value: 81.79743115887055
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.39698537303725
+    - type: cos_sim_spearman
+      value: 88.78668529808967
+    - type: euclidean_pearson
+      value: 88.78863351718252
+    - type: euclidean_spearman
+      value: 88.78668529808967
+    - type: manhattan_pearson
+      value: 88.41678215762478
+    - type: manhattan_spearman
+      value: 88.3827998418763
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 68.49024974161408
+    - type: cos_sim_spearman
+      value: 69.19917146180619
+    - type: euclidean_pearson
+      value: 70.48882819806336
+    - type: euclidean_spearman
+      value: 69.19917146180619
+    - type: manhattan_pearson
+      value: 70.86827961779932
+    - type: manhattan_spearman
+      value: 69.38456983992613
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.31376078795105
+    - type: cos_sim_spearman
+      value: 83.3985199217591
+    - type: euclidean_pearson
+      value: 84.06630133719332
+    - type: euclidean_spearman
+      value: 83.3985199217591
+    - type: manhattan_pearson
+      value: 83.7896654474364
+    - type: manhattan_spearman
+      value: 83.1885039212299
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 85.83161002188668
+    - type: mrr
+      value: 96.19253114351153
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+    metrics:
+    - type: map_at_1
+      value: 48.132999999999996
+    - type: map_at_10
+      value: 58.541
+    - type: map_at_100
+      value: 59.34
+    - type: map_at_1000
+      value: 59.367999999999995
+    - type: map_at_3
+      value: 55.191
+    - type: map_at_5
+      value: 57.084
+    - type: mrr_at_1
+      value: 51.0
+    - type: mrr_at_10
+      value: 59.858
+    - type: mrr_at_100
+      value: 60.474000000000004
+    - type: mrr_at_1000
+      value: 60.501000000000005
+    - type: mrr_at_3
+      value: 57.111000000000004
+    - type: mrr_at_5
+      value: 58.694
+    - type: ndcg_at_1
+      value: 51.0
+    - type: ndcg_at_10
+      value: 63.817
+    - type: ndcg_at_100
+      value: 67.229
+    - type: ndcg_at_1000
+      value: 67.94
+    - type: ndcg_at_3
+      value: 57.896
+    - type: ndcg_at_5
+      value: 60.785999999999994
+    - type: precision_at_1
+      value: 51.0
+    - type: precision_at_10
+      value: 8.933
+    - type: precision_at_100
+      value: 1.0699999999999998
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 23.111
+    - type: precision_at_5
+      value: 15.733
+    - type: recall_at_1
+      value: 48.132999999999996
+    - type: recall_at_10
+      value: 78.922
+    - type: recall_at_100
+      value: 94.167
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 62.806
+    - type: recall_at_5
+      value: 70.078
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.88415841584158
+    - type: cos_sim_ap
+      value: 97.72557886493401
+    - type: cos_sim_f1
+      value: 94.1294530858003
+    - type: cos_sim_precision
+      value: 94.46122860020141
+    - type: cos_sim_recall
+      value: 93.8
+    - type: dot_accuracy
+      value: 99.88415841584158
+    - type: dot_ap
+      value: 97.72557439066108
+    - type: dot_f1
+      value: 94.1294530858003
+    - type: dot_precision
+      value: 94.46122860020141
+    - type: dot_recall
+      value: 93.8
+    - type: euclidean_accuracy
+      value: 99.88415841584158
+    - type: euclidean_ap
+      value: 97.72557439066108
+    - type: euclidean_f1
+      value: 94.1294530858003
+    - type: euclidean_precision
+      value: 94.46122860020141
+    - type: euclidean_recall
+      value: 93.8
+    - type: manhattan_accuracy
+      value: 99.88514851485148
+    - type: manhattan_ap
+      value: 97.73324334051959
+    - type: manhattan_f1
+      value: 94.1825476429288
+    - type: manhattan_precision
+      value: 94.46680080482898
+    - type: manhattan_recall
+      value: 93.89999999999999
+    - type: max_accuracy
+      value: 99.88514851485148
+    - type: max_ap
+      value: 97.73324334051959
+    - type: max_f1
+      value: 94.1825476429288
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 72.8168026381278
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 44.30948635130784
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 54.11268548719803
+    - type: mrr
+      value: 55.08079747050335
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.82885852096243
+    - type: cos_sim_spearman
+      value: 30.800770979226076
+    - type: dot_pearson
+      value: 30.82885608827704
+    - type: dot_spearman
+      value: 30.800770979226076
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.20400000000000001
+    - type: map_at_10
+      value: 1.27
+    - type: map_at_100
+      value: 7.993
+    - type: map_at_1000
+      value: 20.934
+    - type: map_at_3
+      value: 0.469
+    - type: map_at_5
+      value: 0.716
+    - type: mrr_at_1
+      value: 76.0
+    - type: mrr_at_10
+      value: 84.967
+    - type: mrr_at_100
+      value: 84.967
+    - type: mrr_at_1000
+      value: 84.967
+    - type: mrr_at_3
+      value: 83.667
+    - type: mrr_at_5
+      value: 84.967
+    - type: ndcg_at_1
+      value: 69.0
+    - type: ndcg_at_10
+      value: 59.243
+    - type: ndcg_at_100
+      value: 48.784
+    - type: ndcg_at_1000
+      value: 46.966
+    - type: ndcg_at_3
+      value: 64.14
+    - type: ndcg_at_5
+      value: 61.60600000000001
+    - type: precision_at_1
+      value: 76.0
+    - type: precision_at_10
+      value: 62.6
+    - type: precision_at_100
+      value: 50.18
+    - type: precision_at_1000
+      value: 21.026
+    - type: precision_at_3
+      value: 68.667
+    - type: precision_at_5
+      value: 66.0
+    - type: recall_at_1
+      value: 0.20400000000000001
+    - type: recall_at_10
+      value: 1.582
+    - type: recall_at_100
+      value: 11.988
+    - type: recall_at_1000
+      value: 44.994
+    - type: recall_at_3
+      value: 0.515
+    - type: recall_at_5
+      value: 0.844
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+    metrics:
+    - type: map_at_1
+      value: 3.3009999999999997
+    - type: map_at_10
+      value: 11.566
+    - type: map_at_100
+      value: 17.645
+    - type: map_at_1000
+      value: 19.206
+    - type: map_at_3
+      value: 6.986000000000001
+    - type: map_at_5
+      value: 8.716
+    - type: mrr_at_1
+      value: 42.857
+    - type: mrr_at_10
+      value: 58.287
+    - type: mrr_at_100
+      value: 59.111000000000004
+    - type: mrr_at_1000
+      value: 59.111000000000004
+    - type: mrr_at_3
+      value: 55.102
+    - type: mrr_at_5
+      value: 57.449
+    - type: ndcg_at_1
+      value: 39.796
+    - type: ndcg_at_10
+      value: 29.059
+    - type: ndcg_at_100
+      value: 40.629
+    - type: ndcg_at_1000
+      value: 51.446000000000005
+    - type: ndcg_at_3
+      value: 36.254999999999995
+    - type: ndcg_at_5
+      value: 32.216
+    - type: precision_at_1
+      value: 42.857
+    - type: precision_at_10
+      value: 23.469
+    - type: precision_at_100
+      value: 8.041
+    - type: precision_at_1000
+      value: 1.551
+    - type: precision_at_3
+      value: 36.735
+    - type: precision_at_5
+      value: 30.203999999999997
+    - type: recall_at_1
+      value: 3.3009999999999997
+    - type: recall_at_10
+      value: 17.267
+    - type: recall_at_100
+      value: 49.36
+    - type: recall_at_1000
+      value: 83.673
+    - type: recall_at_3
+      value: 8.049000000000001
+    - type: recall_at_5
+      value: 11.379999999999999
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 88.7576
+    - type: ap
+      value: 35.52110634325751
+    - type: f1
+      value: 74.14476947482417
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 73.52009054895304
+    - type: f1
+      value: 73.81407409876577
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 54.35358706465052
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 83.65619598259522
+    - type: cos_sim_ap
+      value: 65.824087818991
+    - type: cos_sim_f1
+      value: 61.952620244077536
+    - type: cos_sim_precision
+      value: 56.676882661996494
+    - type: cos_sim_recall
+      value: 68.311345646438
+    - type: dot_accuracy
+      value: 83.65619598259522
+    - type: dot_ap
+      value: 65.82406256999921
+    - type: dot_f1
+      value: 61.952620244077536
+    - type: dot_precision
+      value: 56.676882661996494
+    - type: dot_recall
+      value: 68.311345646438
+    - type: euclidean_accuracy
+      value: 83.65619598259522
+    - type: euclidean_ap
+      value: 65.82409143427542
+    - type: euclidean_f1
+      value: 61.952620244077536
+    - type: euclidean_precision
+      value: 56.676882661996494
+    - type: euclidean_recall
+      value: 68.311345646438
+    - type: manhattan_accuracy
+      value: 83.4296954163438
+    - type: manhattan_ap
+      value: 65.20662449614932
+    - type: manhattan_f1
+      value: 61.352885525070946
+    - type: manhattan_precision
+      value: 55.59365623660523
+    - type: manhattan_recall
+      value: 68.44327176781002
+    - type: max_accuracy
+      value: 83.65619598259522
+    - type: max_ap
+      value: 65.82409143427542
+    - type: max_f1
+      value: 61.952620244077536
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.90119144642372
+    - type: cos_sim_ap
+      value: 84.04753852793387
+    - type: cos_sim_f1
+      value: 76.27737226277372
+    - type: cos_sim_precision
+      value: 73.86757068667052
+    - type: cos_sim_recall
+      value: 78.84970742223591
+    - type: dot_accuracy
+      value: 87.90119144642372
+    - type: dot_ap
+      value: 84.04753668117337
+    - type: dot_f1
+      value: 76.27737226277372
+    - type: dot_precision
+      value: 73.86757068667052
+    - type: dot_recall
+      value: 78.84970742223591
+    - type: euclidean_accuracy
+      value: 87.90119144642372
+    - type: euclidean_ap
+      value: 84.04754553468206
+    - type: euclidean_f1
+      value: 76.27737226277372
+    - type: euclidean_precision
+      value: 73.86757068667052
+    - type: euclidean_recall
+      value: 78.84970742223591
+    - type: manhattan_accuracy
+      value: 87.87014398261343
+    - type: manhattan_ap
+      value: 84.05164646221583
+    - type: manhattan_f1
+      value: 76.31392706820128
+    - type: manhattan_precision
+      value: 73.91586694566708
+    - type: manhattan_recall
+      value: 78.87280566676932
+    - type: max_accuracy
+      value: 87.90119144642372
+    - type: max_ap
+      value: 84.05164646221583
+    - type: max_f1
+      value: 76.31392706820128
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/AFQMC
+      name: MTEB AFQMC
+      config: default
+      split: validation
+      revision: b44c3b011063adb25877c13823db83bb193913c4
+    metrics:
+    - type: cos_sim_pearson
+      value: 52.3123511272669
+    - type: cos_sim_spearman
+      value: 55.73207493107254
+    - type: euclidean_pearson
+      value: 53.95847274621819
+    - type: euclidean_spearman
+      value: 55.73207493107254
+    - type: manhattan_pearson
+      value: 53.720688490931124
+    - type: manhattan_spearman
+      value: 55.453911938689
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/ATEC
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
+    metrics:
+    - type: cos_sim_pearson
+      value: 50.787428883419864
+    - type: cos_sim_spearman
+      value: 53.97343607668934
+    - type: euclidean_pearson
+      value: 55.12379889727461
+    - type: euclidean_spearman
+      value: 53.97343945403084
+    - type: manhattan_pearson
+      value: 54.95369694130932
+    - type: manhattan_spearman
+      value: 53.74165246349166
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 53.49
+    - type: f1
+      value: 51.576550662258434
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
+    metrics:
+    - type: cos_sim_pearson
+      value: 63.78770644319529
+    - type: cos_sim_spearman
+      value: 65.08813140587463
+    - type: euclidean_pearson
+      value: 63.92948559310832
+    - type: euclidean_spearman
+      value: 65.08813486997627
+    - type: manhattan_pearson
+      value: 63.55967028084246
+    - type: manhattan_spearman
+      value: 64.69692694499825
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
+    metrics:
+    - type: v_measure
+      value: 44.23533333311907
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
+    metrics:
+    - type: v_measure
+      value: 43.01114481307774
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
+    metrics:
+    - type: map
+      value: 86.4349853821696
+    - type: mrr
+      value: 88.80150793650795
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: 23d186750531a14a0357ca22cd92d712fd512ea0
+    metrics:
+    - type: map
+      value: 87.56417400982208
+    - type: mrr
+      value: 89.85813492063491
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
+    metrics:
+    - type: map_at_1
+      value: 24.871
+    - type: map_at_10
+      value: 37.208999999999996
+    - type: map_at_100
+      value: 38.993
+    - type: map_at_1000
+      value: 39.122
+    - type: map_at_3
+      value: 33.2
+    - type: map_at_5
+      value: 35.33
+    - type: mrr_at_1
+      value: 37.884
+    - type: mrr_at_10
+      value: 46.189
+    - type: mrr_at_100
+      value: 47.147
+    - type: mrr_at_1000
+      value: 47.195
+    - type: mrr_at_3
+      value: 43.728
+    - type: mrr_at_5
+      value: 44.994
+    - type: ndcg_at_1
+      value: 37.884
+    - type: ndcg_at_10
+      value: 43.878
+    - type: ndcg_at_100
+      value: 51.002
+    - type: ndcg_at_1000
+      value: 53.161
+    - type: ndcg_at_3
+      value: 38.729
+    - type: ndcg_at_5
+      value: 40.628
+    - type: precision_at_1
+      value: 37.884
+    - type: precision_at_10
+      value: 9.75
+    - type: precision_at_100
+      value: 1.558
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 21.964
+    - type: precision_at_5
+      value: 15.719
+    - type: recall_at_1
+      value: 24.871
+    - type: recall_at_10
+      value: 54.615
+    - type: recall_at_100
+      value: 84.276
+    - type: recall_at_1000
+      value: 98.578
+    - type: recall_at_3
+      value: 38.936
+    - type: recall_at_5
+      value: 45.061
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
+    metrics:
+    - type: cos_sim_accuracy
+      value: 76.12748045700542
+    - type: cos_sim_ap
+      value: 84.47948419710998
+    - type: cos_sim_f1
+      value: 77.88108108108108
+    - type: cos_sim_precision
+      value: 72.43112809169516
+    - type: cos_sim_recall
+      value: 84.21790974982464
+    - type: dot_accuracy
+      value: 76.12748045700542
+    - type: dot_ap
+      value: 84.4933237839786
+    - type: dot_f1
+      value: 77.88108108108108
+    - type: dot_precision
+      value: 72.43112809169516
+    - type: dot_recall
+      value: 84.21790974982464
+    - type: euclidean_accuracy
+      value: 76.12748045700542
+    - type: euclidean_ap
+      value: 84.47947997540409
+    - type: euclidean_f1
+      value: 77.88108108108108
+    - type: euclidean_precision
+      value: 72.43112809169516
+    - type: euclidean_recall
+      value: 84.21790974982464
+    - type: manhattan_accuracy
+      value: 75.40589296452195
+    - type: manhattan_ap
+      value: 83.74383956930585
+    - type: manhattan_f1
+      value: 77.0983342289092
+    - type: manhattan_precision
+      value: 71.34049323786795
+    - type: manhattan_recall
+      value: 83.86719663315408
+    - type: max_accuracy
+      value: 76.12748045700542
+    - type: max_ap
+      value: 84.4933237839786
+    - type: max_f1
+      value: 77.88108108108108
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: 1271c7809071a13532e05f25fb53511ffce77117
+    metrics:
+    - type: map_at_1
+      value: 66.781
+    - type: map_at_10
+      value: 74.539
+    - type: map_at_100
+      value: 74.914
+    - type: map_at_1000
+      value: 74.921
+    - type: map_at_3
+      value: 72.734
+    - type: map_at_5
+      value: 73.788
+    - type: mrr_at_1
+      value: 66.913
+    - type: mrr_at_10
+      value: 74.543
+    - type: mrr_at_100
+      value: 74.914
+    - type: mrr_at_1000
+      value: 74.921
+    - type: mrr_at_3
+      value: 72.831
+    - type: mrr_at_5
+      value: 73.76899999999999
+    - type: ndcg_at_1
+      value: 67.018
+    - type: ndcg_at_10
+      value: 78.34299999999999
+    - type: ndcg_at_100
+      value: 80.138
+    - type: ndcg_at_1000
+      value: 80.322
+    - type: ndcg_at_3
+      value: 74.667
+    - type: ndcg_at_5
+      value: 76.518
+    - type: precision_at_1
+      value: 67.018
+    - type: precision_at_10
+      value: 9.115
+    - type: precision_at_100
+      value: 0.996
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 26.906000000000002
+    - type: precision_at_5
+      value: 17.092
+    - type: recall_at_1
+      value: 66.781
+    - type: recall_at_10
+      value: 90.253
+    - type: recall_at_100
+      value: 98.52499999999999
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_3
+      value: 80.05799999999999
+    - type: recall_at_5
+      value: 84.615
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/DuRetrieval
+      name: MTEB DuRetrieval
+      config: default
+      split: dev
+      revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
+    metrics:
+    - type: map_at_1
+      value: 24.528
+    - type: map_at_10
+      value: 76.304
+    - type: map_at_100
+      value: 79.327
+    - type: map_at_1000
+      value: 79.373
+    - type: map_at_3
+      value: 52.035
+    - type: map_at_5
+      value: 66.074
+    - type: mrr_at_1
+      value: 86.05000000000001
+    - type: mrr_at_10
+      value: 90.74
+    - type: mrr_at_100
+      value: 90.809
+    - type: mrr_at_1000
+      value: 90.81099999999999
+    - type: mrr_at_3
+      value: 90.30799999999999
+    - type: mrr_at_5
+      value: 90.601
+    - type: ndcg_at_1
+      value: 86.05000000000001
+    - type: ndcg_at_10
+      value: 84.518
+    - type: ndcg_at_100
+      value: 87.779
+    - type: ndcg_at_1000
+      value: 88.184
+    - type: ndcg_at_3
+      value: 82.339
+    - type: ndcg_at_5
+      value: 81.613
+    - type: precision_at_1
+      value: 86.05000000000001
+    - type: precision_at_10
+      value: 40.945
+    - type: precision_at_100
+      value: 4.787
+    - type: precision_at_1000
+      value: 0.48900000000000005
+    - type: precision_at_3
+      value: 74.117
+    - type: precision_at_5
+      value: 62.86000000000001
+    - type: recall_at_1
+      value: 24.528
+    - type: recall_at_10
+      value: 86.78
+    - type: recall_at_100
+      value: 97.198
+    - type: recall_at_1000
+      value: 99.227
+    - type: recall_at_3
+      value: 54.94799999999999
+    - type: recall_at_5
+      value: 72.053
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/EcomRetrieval
+      name: MTEB EcomRetrieval
+      config: default
+      split: dev
+      revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
+    metrics:
+    - type: map_at_1
+      value: 52.1
+    - type: map_at_10
+      value: 62.502
+    - type: map_at_100
+      value: 63.026
+    - type: map_at_1000
+      value: 63.04
+    - type: map_at_3
+      value: 59.782999999999994
+    - type: map_at_5
+      value: 61.443000000000005
+    - type: mrr_at_1
+      value: 52.1
+    - type: mrr_at_10
+      value: 62.502
+    - type: mrr_at_100
+      value: 63.026
+    - type: mrr_at_1000
+      value: 63.04
+    - type: mrr_at_3
+      value: 59.782999999999994
+    - type: mrr_at_5
+      value: 61.443000000000005
+    - type: ndcg_at_1
+      value: 52.1
+    - type: ndcg_at_10
+      value: 67.75999999999999
+    - type: ndcg_at_100
+      value: 70.072
+    - type: ndcg_at_1000
+      value: 70.441
+    - type: ndcg_at_3
+      value: 62.28
+    - type: ndcg_at_5
+      value: 65.25800000000001
+    - type: precision_at_1
+      value: 52.1
+    - type: precision_at_10
+      value: 8.43
+    - type: precision_at_100
+      value: 0.946
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 23.166999999999998
+    - type: precision_at_5
+      value: 15.340000000000002
+    - type: recall_at_1
+      value: 52.1
+    - type: recall_at_10
+      value: 84.3
+    - type: recall_at_100
+      value: 94.6
+    - type: recall_at_1000
+      value: 97.5
+    - type: recall_at_3
+      value: 69.5
+    - type: recall_at_5
+      value: 76.7
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/IFlyTek-classification
+      name: MTEB IFlyTek
+      config: default
+      split: validation
+      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
+    metrics:
+    - type: accuracy
+      value: 52.04309349749903
+    - type: f1
+      value: 39.91893257315586
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/JDReview-classification
+      name: MTEB JDReview
+      config: default
+      split: test
+      revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
+    metrics:
+    - type: accuracy
+      value: 85.60975609756099
+    - type: ap
+      value: 54.30148799475452
+    - type: f1
+      value: 80.55899583002706
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/LCQMC
+      name: MTEB LCQMC
+      config: default
+      split: test
+      revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
+    metrics:
+    - type: cos_sim_pearson
+      value: 66.80471387011771
+    - type: cos_sim_spearman
+      value: 72.69179486905233
+    - type: euclidean_pearson
+      value: 71.32341962627513
+    - type: euclidean_spearman
+      value: 72.69179043377405
+    - type: manhattan_pearson
+      value: 71.06180379791572
+    - type: manhattan_spearman
+      value: 72.400125270369
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/Mmarco-reranking
+      name: MTEB MMarcoReranking
+      config: default
+      split: dev
+      revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
+    metrics:
+    - type: map
+      value: 27.9616280919871
+    - type: mrr
+      value: 26.544047619047618
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MMarcoRetrieval
+      name: MTEB MMarcoRetrieval
+      config: default
+      split: dev
+      revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
+    metrics:
+    - type: map_at_1
+      value: 68.32300000000001
+    - type: map_at_10
+      value: 77.187
+    - type: map_at_100
+      value: 77.496
+    - type: map_at_1000
+      value: 77.503
+    - type: map_at_3
+      value: 75.405
+    - type: map_at_5
+      value: 76.539
+    - type: mrr_at_1
+      value: 70.616
+    - type: mrr_at_10
+      value: 77.703
+    - type: mrr_at_100
+      value: 77.97699999999999
+    - type: mrr_at_1000
+      value: 77.984
+    - type: mrr_at_3
+      value: 76.139
+    - type: mrr_at_5
+      value: 77.125
+    - type: ndcg_at_1
+      value: 70.616
+    - type: ndcg_at_10
+      value: 80.741
+    - type: ndcg_at_100
+      value: 82.123
+    - type: ndcg_at_1000
+      value: 82.32300000000001
+    - type: ndcg_at_3
+      value: 77.35600000000001
+    - type: ndcg_at_5
+      value: 79.274
+    - type: precision_at_1
+      value: 70.616
+    - type: precision_at_10
+      value: 9.696
+    - type: precision_at_100
+      value: 1.038
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 29.026000000000003
+    - type: precision_at_5
+      value: 18.433
+    - type: recall_at_1
+      value: 68.32300000000001
+    - type: recall_at_10
+      value: 91.186
+    - type: recall_at_100
+      value: 97.439
+    - type: recall_at_1000
+      value: 99.004
+    - type: recall_at_3
+      value: 82.218
+    - type: recall_at_5
+      value: 86.797
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (zh-CN)
+      config: zh-CN
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 74.78143913920646
+    - type: f1
+      value: 72.6141122227626
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (zh-CN)
+      config: zh-CN
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 76.98722259583053
+    - type: f1
+      value: 76.5974920207624
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MedicalRetrieval
+      name: MTEB MedicalRetrieval
+      config: default
+      split: dev
+      revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
+    metrics:
+    - type: map_at_1
+      value: 51.800000000000004
+    - type: map_at_10
+      value: 57.938
+    - type: map_at_100
+      value: 58.494
+    - type: map_at_1000
+      value: 58.541
+    - type: map_at_3
+      value: 56.617
+    - type: map_at_5
+      value: 57.302
+    - type: mrr_at_1
+      value: 51.800000000000004
+    - type: mrr_at_10
+      value: 57.938
+    - type: mrr_at_100
+      value: 58.494
+    - type: mrr_at_1000
+      value: 58.541
+    - type: mrr_at_3
+      value: 56.617
+    - type: mrr_at_5
+      value: 57.302
+    - type: ndcg_at_1
+      value: 51.800000000000004
+    - type: ndcg_at_10
+      value: 60.891
+    - type: ndcg_at_100
+      value: 63.897000000000006
+    - type: ndcg_at_1000
+      value: 65.231
+    - type: ndcg_at_3
+      value: 58.108000000000004
+    - type: ndcg_at_5
+      value: 59.343
+    - type: precision_at_1
+      value: 51.800000000000004
+    - type: precision_at_10
+      value: 7.02
+    - type: precision_at_100
+      value: 0.8500000000000001
+    - type: precision_at_1000
+      value: 0.096
+    - type: precision_at_3
+      value: 20.8
+    - type: precision_at_5
+      value: 13.08
+    - type: recall_at_1
+      value: 51.800000000000004
+    - type: recall_at_10
+      value: 70.19999999999999
+    - type: recall_at_100
+      value: 85.0
+    - type: recall_at_1000
+      value: 95.7
+    - type: recall_at_3
+      value: 62.4
+    - type: recall_at_5
+      value: 65.4
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/MultilingualSentiment-classification
+      name: MTEB MultilingualSentiment
+      config: default
+      split: validation
+      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
+    metrics:
+    - type: accuracy
+      value: 80.39333333333335
+    - type: f1
+      value: 80.42683132366277
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/OCNLI
+      name: MTEB Ocnli
+      config: default
+      split: validation
+      revision: 66e76a618a34d6d565d5538088562851e6daa7ec
+    metrics:
+    - type: cos_sim_accuracy
+      value: 70.7634001082837
+    - type: cos_sim_ap
+      value: 74.97527385556558
+    - type: cos_sim_f1
+      value: 72.77277277277277
+    - type: cos_sim_precision
+      value: 69.17221693625119
+    - type: cos_sim_recall
+      value: 76.76874340021119
+    - type: dot_accuracy
+      value: 70.7634001082837
+    - type: dot_ap
+      value: 74.97527385556558
+    - type: dot_f1
+      value: 72.77277277277277
+    - type: dot_precision
+      value: 69.17221693625119
+    - type: dot_recall
+      value: 76.76874340021119
+    - type: euclidean_accuracy
+      value: 70.7634001082837
+    - type: euclidean_ap
+      value: 74.97527385556558
+    - type: euclidean_f1
+      value: 72.77277277277277
+    - type: euclidean_precision
+      value: 69.17221693625119
+    - type: euclidean_recall
+      value: 76.76874340021119
+    - type: manhattan_accuracy
+      value: 69.89713048186248
+    - type: manhattan_ap
+      value: 74.25943370061067
+    - type: manhattan_f1
+      value: 72.17268887846082
+    - type: manhattan_precision
+      value: 64.94932432432432
+    - type: manhattan_recall
+      value: 81.20380147835269
+    - type: max_accuracy
+      value: 70.7634001082837
+    - type: max_ap
+      value: 74.97527385556558
+    - type: max_f1
+      value: 72.77277277277277
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/OnlineShopping-classification
+      name: MTEB OnlineShopping
+      config: default
+      split: test
+      revision: e610f2ebd179a8fda30ae534c3878750a96db120
+    metrics:
+    - type: accuracy
+      value: 92.92000000000002
+    - type: ap
+      value: 91.98475625106201
+    - type: f1
+      value: 92.91841470541901
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/PAWSX
+      name: MTEB PAWSX
+      config: default
+      split: test
+      revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
+    metrics:
+    - type: cos_sim_pearson
+      value: 14.383440096352668
+    - type: cos_sim_spearman
+      value: 16.306924065606417
+    - type: euclidean_pearson
+      value: 18.41761420026285
+    - type: euclidean_spearman
+      value: 16.306657048204574
+    - type: manhattan_pearson
+      value: 18.4377010794545
+    - type: manhattan_spearman
+      value: 16.36919038809279
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/QBQTC
+      name: MTEB QBQTC
+      config: default
+      split: test
+      revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.95106420311818
+    - type: cos_sim_spearman
+      value: 34.89277148116508
+    - type: euclidean_pearson
+      value: 32.94933182954164
+    - type: euclidean_spearman
+      value: 34.89280064539983
+    - type: manhattan_pearson
+      value: 32.86089069741366
+    - type: manhattan_spearman
+      value: 34.7932921716507
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (zh)
+      config: zh
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 67.41628669863584
+    - type: cos_sim_spearman
+      value: 67.87238206703478
+    - type: euclidean_pearson
+      value: 67.67834985311778
+    - type: euclidean_spearman
+      value: 67.87238206703478
+    - type: manhattan_pearson
+      value: 68.23423896742973
+    - type: manhattan_spearman
+      value: 68.27069260687092
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/STSB
+      name: MTEB STSB
+      config: default
+      split: test
+      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.31628954400037
+    - type: cos_sim_spearman
+      value: 76.83296022489624
+    - type: euclidean_pearson
+      value: 76.69680425261211
+    - type: euclidean_spearman
+      value: 76.83287843321102
+    - type: manhattan_pearson
+      value: 76.65603163327958
+    - type: manhattan_spearman
+      value: 76.80803503360451
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/T2Reranking
+      name: MTEB T2Reranking
+      config: default
+      split: dev
+      revision: 76631901a18387f85eaa53e5450019b87ad58ef9
+    metrics:
+    - type: map
+      value: 66.73038448968596
+    - type: mrr
+      value: 77.26510193334836
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/T2Retrieval
+      name: MTEB T2Retrieval
+      config: default
+      split: dev
+      revision: 8731a845f1bf500a4f111cf1070785c793d10e64
+    metrics:
+    - type: map_at_1
+      value: 28.157
+    - type: map_at_10
+      value: 79.00399999999999
+    - type: map_at_100
+      value: 82.51899999999999
+    - type: map_at_1000
+      value: 82.577
+    - type: map_at_3
+      value: 55.614
+    - type: map_at_5
+      value: 68.292
+    - type: mrr_at_1
+      value: 91.167
+    - type: mrr_at_10
+      value: 93.391
+    - type: mrr_at_100
+      value: 93.467
+    - type: mrr_at_1000
+      value: 93.47
+    - type: mrr_at_3
+      value: 93.001
+    - type: mrr_at_5
+      value: 93.254
+    - type: ndcg_at_1
+      value: 91.167
+    - type: ndcg_at_10
+      value: 86.155
+    - type: ndcg_at_100
+      value: 89.425
+    - type: ndcg_at_1000
+      value: 89.983
+    - type: ndcg_at_3
+      value: 87.516
+    - type: ndcg_at_5
+      value: 86.148
+    - type: precision_at_1
+      value: 91.167
+    - type: precision_at_10
+      value: 42.697
+    - type: precision_at_100
+      value: 5.032
+    - type: precision_at_1000
+      value: 0.516
+    - type: precision_at_3
+      value: 76.45100000000001
+    - type: precision_at_5
+      value: 64.051
+    - type: recall_at_1
+      value: 28.157
+    - type: recall_at_10
+      value: 84.974
+    - type: recall_at_100
+      value: 95.759
+    - type: recall_at_1000
+      value: 98.583
+    - type: recall_at_3
+      value: 57.102
+    - type: recall_at_5
+      value: 71.383
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/TNews-classification
+      name: MTEB TNews
+      config: default
+      split: validation
+      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
+    metrics:
+    - type: accuracy
+      value: 55.031
+    - type: f1
+      value: 53.07992810732314
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringP2P
+      name: MTEB ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: 5798586b105c0434e4f0fe5e767abe619442cf93
+    metrics:
+    - type: v_measure
+      value: 72.80915114296552
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringS2S
+      name: MTEB ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
+    metrics:
+    - type: v_measure
+      value: 70.86374654127641
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/VideoRetrieval
+      name: MTEB VideoRetrieval
+      config: default
+      split: dev
+      revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
+    metrics:
+    - type: map_at_1
+      value: 63.6
+    - type: map_at_10
+      value: 72.673
+    - type: map_at_100
+      value: 73.05199999999999
+    - type: map_at_1000
+      value: 73.057
+    - type: map_at_3
+      value: 70.833
+    - type: map_at_5
+      value: 72.05799999999999
+    - type: mrr_at_1
+      value: 63.6
+    - type: mrr_at_10
+      value: 72.673
+    - type: mrr_at_100
+      value: 73.05199999999999
+    - type: mrr_at_1000
+      value: 73.057
+    - type: mrr_at_3
+      value: 70.833
+    - type: mrr_at_5
+      value: 72.05799999999999
+    - type: ndcg_at_1
+      value: 63.6
+    - type: ndcg_at_10
+      value: 76.776
+    - type: ndcg_at_100
+      value: 78.52900000000001
+    - type: ndcg_at_1000
+      value: 78.696
+    - type: ndcg_at_3
+      value: 73.093
+    - type: ndcg_at_5
+      value: 75.288
+    - type: precision_at_1
+      value: 63.6
+    - type: precision_at_10
+      value: 8.95
+    - type: precision_at_100
+      value: 0.975
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 26.533
+    - type: precision_at_5
+      value: 16.98
+    - type: recall_at_1
+      value: 63.6
+    - type: recall_at_10
+      value: 89.5
+    - type: recall_at_100
+      value: 97.5
+    - type: recall_at_1000
+      value: 98.9
+    - type: recall_at_3
+      value: 79.60000000000001
+    - type: recall_at_5
+      value: 84.89999999999999
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/waimai-classification
+      name: MTEB Waimai
+      config: default
+      split: test
+      revision: 339287def212450dcaa9df8c22bf93e9980c7023
+    metrics:
+    - type: accuracy
+      value: 89.39999999999999
+    - type: ap
+      value: 75.52087544076016
+    - type: f1
+      value: 87.7629629899278
+---
+
+<p align="center"><b>GME: General Multimodal Embeddings</b></p>
+
+## GME-Qwen2VL-7B
+
+We are excited to present `GME-Qwen2VL` models, our first generation **multimodal embedding models** for text and images,
+which are based on advanced [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) multimodal large language models (MLLMs).
+
+The `GME-Qwen2VL` models support three input forms: **text**, **image**, and **image-text pair**, all of which can produce universal vector representations and have powerful retrieval performance.
+
+- **High Performance**: Achieves state-of-the-art (SOTA) results in our universal multimodal retrieval benchmark (**UMRB**) and strong **MTEB** evaluation scores.
+- **Dynamic Image Resolution**: Benefiting from `Qwen2-VL` and our training data, GME models support dynamic resolution image input.
+Our models are able to perform leadingly in the **visual document retrieval** task which requires fine-grained understanding of document screenshots.
+You can control to balance performance and efficiency.
+
+**Developed by**: Tongyi Lab, Alibaba Group
+
+**Paper**: GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
+
+
+## Model List
+| Models | Model Size | Max Seq. Length | Dimension | MTEB-en| UMRB |
+|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: |
+|[`gme-Qwen2VL-2B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct) | 2.21B | 32768 | 1536 | - | 64.45 |
+|[`gme-Qwen2VL-7B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) | 8.29B | 32768 | 3584 | - | 67.02 |
+
+## Usage 
+
+**Use with custom code**
+
+```python
+# You can find the script gme_inference.py in https://huggingface.co/Alibaba-NLP/gme-Qwen2VL-2B/blob/main/scripts/gme_inference.py
+from gme_inference import GmeQwen2VL
+
+texts = [
+    "What kind of car is this?",
+    "The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023."
+]
+images = [
+    'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',
+    'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
+]
+
+gme = GmeQwen2VL("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct")
+
+# Single-modal embedding
+e_text = gme.get_text_embeddings(texts=texts)
+e_image = gme.get_image_embeddings(images=images)
+print((e_text * e_image).sum(-1))
+## tensor([0.2281, 0.6001], dtype=torch.float16)
+
+# How to set embedding instruction
+e_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')
+# If is_query=False, we always use the default instruction.
+e_corpus = gme.get_image_embeddings(images=images, is_query=False)
+print((e_query * e_corpus).sum(-1))
+## tensor([0.2433, 0.7051], dtype=torch.float16)
+
+# Fused-modal embedding
+e_fused = gme.get_fused_embeddings(texts=texts, images=images)
+print((e_fused[0] * e_fused[1]).sum())
+## tensor(0.6108, dtype=torch.float16)
+
+```
+
+## Evaluation
+
+We validated the performance on our universal multimodal retrieval benchmark (**UMRB**) among others.
+
+|                    |      | Single-modal |           | Cross-modal |             |           | Fused-modal |            |            |             |  Avg.      |
+|--------------------|------|:------------:|:---------:|:-----------:|:-----------:|:---------:|:-----------:|:----------:|:----------:|:-----------:|:----------:|
+|                    |      | T→T (16)     |  I→I (1)  |  T→I (4)    |  T→VD (10)  |  I→T (4)  |  T→IT (2)   |  IT→T (5)  |  IT→I (2)  |  IT→IT (3)  |  (47)      |
+| VISTA              | 0.2B | 55.15        | **31.98** | 32.88       | 10.12       | 31.23     | 45.81       | 53.32      | 8.97       | 26.26       | 36.74      |
+| CLIP-SF            | 0.4B | 39.75        | 31.42     | 59.05       | 24.09       | 62.95     | 66.41       | 53.32      | 34.9       | 55.65       | 43.24      |
+| One-Peace          | 4B   | 43.54        | 31.27     | 61.38       | 42.9        | 65.59     | 42.72       | 28.29      | 6.73       | 23.41       | 42.03      |
+| DSE                | 4.2B | 48.94        | 27.92     | 40.75       | 78.21       | 52.54     | 49.62       | 35.44      | 8.36       | 40.18       | 50.63      |
+| E5-V               | 8.4B | 52.41        | 27.36     | 46.56       | 41.22       | 47.95     | 54.13       | 32.9       | 23.17      | 7.23        | 42.48      |
+| **GME-Qwen2VL-2B** | 2.2B | 55.93 | 29.86	| 57.36	| 87.84	| **61.93** |	76.47	| 64.58	 | 37.02	| 66.47 | 64.45 |
+| **GME-Qwen2VL-7B** | 8.3B | **58.19** | 31.89 | **61.35** | **89.92** | 60.83 | **80.94** | **66.18** | **42.56** | **73.62** | **67.02** |
+
+The [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) English tab shows the text embeddings performence of our model.
+
+**More detailed experimental results can be found in the [paper](https://arxiv.org/pdf/2407.19669)**.
+
+
+## Limitations
+
+- **Single Image Input**: In `Qwen2-VL`, an image could be converted into a very large number of visual tokens. We limit the number of visual tokens to 1024 to obtain a good training efficiency.
+Due to the lack of relevant data, our models and evaluations retain one single image.
+- **English-only Training**: Our models are trained on english data only. Although the `Qwen2-VL` models are multilingual, the multilingual-multimodal embedding performance are not guaranteed.
+
+We will extend to multi-image input, image-text interleaved data as well as multilingual data in the future version.
+
+
+## Redistribution and Use
+
+We welcome and appreciate various applications of GME models and further improvements to the GME models themselves.
+Following Llama license, 
+1. if you distribute or make available the GME models (or any derivative works thereof),
+or a product or service (including another AI model) that contains any of them,
+you shall prominently display “Built with GME” on a related website, user interface, blogpost, about page, or product documentation;
+2. if you use the GME models or any outputs or results of them to create, train, fine tune, or otherwise improve an AI model,
+which is distributed or made available, you shall also include “GME” at the beginning of any such AI model name.
+
+
+
+## Citation
+If you find our paper or models helpful, please consider cite:
+
+```
+@misc{zhang2024gme,
+      title={GME: Improving Universal Multimodal Retrieval by Multimodal LLMs}, 
+      author={Zhang, Xin and Zhang, Yanzhao and Xie, Wen and Li, Mingxin and Dai, Ziqi and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Li, Wenjie and Zhang, Min},
+      year={2024},
+      eprint={2412.xxxxx},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.xxxxx}, 
+}
+```

added_tokens.json

@@ -0,0 +1,16 @@
+{
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

config.json

@@ -0,0 +1,47 @@
+{
+  "_name_or_path": "gme-Qwen2-VL-2B-Instruct",
+  "architectures": [
+    "Qwen2VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2_vl",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "type": "mrope"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.0.dev0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1536,
+    "in_chans": 3,
+    "model_type": "qwen2_vl",
+    "spatial_patch_size": 14
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.1,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.45.0.dev0"
+}

gme_inference.py

@@ -0,0 +1,329 @@
+from __future__ import annotations
+
+import logging
+import math
+import os
+from typing import Dict, List, Optional
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from tqdm.autonotebook import tqdm
+from transformers import AutoModelForVision2Seq, AutoProcessor
+
+
+class GmeQwen2VL:
+    def __init__(
+        self,
+        model_name: str = "Alibaba-NLP/gme-Qwen2-VL-2B-Instruct",
+        model_path: Optional[str] = None,
+        device: str = "cuda" if torch.cuda.is_available() else "cpu",
+        min_image_tokens=256,
+        max_image_tokens=1280,
+        max_length=1800,
+        **kwargs,
+    ) -> None:
+        model_name = model_path or model_name
+        self.base = AutoModelForVision2Seq.from_pretrained(
+            model_name, torch_dtype=torch.float16, **kwargs
+        )
+        self.base.eval()
+        self.normalize = True
+        self.device = device
+        min_pixels = min_image_tokens * 28 * 28
+        max_pixels = max_image_tokens * 28 * 28
+        self.max_length = max_length
+        self.processor = AutoProcessor.from_pretrained(
+            model_name, min_pixels=min_pixels, max_pixels=max_pixels, **kwargs
+        )
+        self.processor.tokenizer.padding_side = 'right'
+        self.defualt_instruction = 'You are a helpful assistant.'
+        self.sep = ' '
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        # pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        # video_grid_thw: Optional[torch.LongTensor] = None,
+        pooling_mask: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.base.model.embed_tokens(input_ids)
+            if pixel_values is not None:
+                pixel_values = pixel_values.type(self.base.visual.get_dtype())
+                image_embeds = self.base.visual(pixel_values, grid_thw=image_grid_thw).to(inputs_embeds.device)
+                image_mask = input_ids == self.base.config.image_token_id
+                inputs_embeds[image_mask] = image_embeds
+            # if pixel_values_videos is not None:
+            #     pixel_values_videos = pixel_values_videos.type(self.base.visual.get_dtype())
+            #     video_embeds = self.base.visual(pixel_values_videos, grid_thw=video_grid_thw).to(inputs_embeds.device)
+            #     video_mask = input_ids == self.base.config.video_token_id
+            #     inputs_embeds[video_mask] = video_embeds
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+        outputs = self.base.model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+        )
+
+        pooling_mask = attention_mask if pooling_mask is None else pooling_mask
+        left_padding = (pooling_mask[:, -1].sum() == pooling_mask.shape[0])  # TODO
+        if left_padding:
+            embeddings = outputs.last_hidden_state[:, -1]
+        else:
+            sequence_lengths = pooling_mask.sum(dim=1) - 1
+            batch_size = outputs.last_hidden_state.shape[0]
+            embeddings = outputs.last_hidden_state[torch.arange(
+                batch_size, device=outputs.last_hidden_state.device
+            ), sequence_lengths]
+        if self.normalize:
+            embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+        return embeddings.contiguous()
+
+    def embed(self, texts: list[str], images: list[Image.Image], is_query=True, instruction=None, **kwargs):
+        self.base.to(self.device)
+        # Inputs must be batched
+        input_texts, input_images = list(), list()
+        for t, i in zip(texts, images):
+            if not is_query or instruction is None:
+                instruction = self.defualt_instruction
+            input_str = ''
+            if i is None:
+                input_images = None  # All examples in the same batch are consistent
+            else:
+                input_str += '<|vision_start|><|image_pad|><|vision_end|>'
+                i = fetch_image(i)
+                input_images.append(i)
+            if t is not None:
+                input_str += t
+            msg = f'<|im_start|>system\n{instruction}<|im_end|>\n<|im_start|>user\n{input_str}<|im_end|>\n<|im_start|>assistant\n<|endoftext|>'
+            input_texts.append(msg)
+
+        inputs = self.processor(
+            text=input_texts,
+            images=input_images,
+            padding=True,
+            truncation=True,
+            max_length=self.max_length,
+            return_tensors='pt'
+        )
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}  # TODO
+        with torch.no_grad():
+            embeddings = self.forward(**inputs)
+        return embeddings
+
+    def encode(self, sentences: list[str], *, prompt_name=None, **kwargs):
+        return self.get_fused_embeddings(texts=sentences, prompt_name=prompt_name, **kwargs)
+
+    def encode_queries(self, queries: List[str], **kwargs):
+        embeddings = self.encode(queries, **kwargs)
+        return embeddings
+
+    def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):
+        if type(corpus) is dict:
+            sentences = [
+                (corpus["title"][i] + self.sep + corpus["text"][i]).strip()
+                if "title" in corpus
+                else corpus["text"][i].strip()
+                for i in range(len(corpus["text"]))
+            ]
+        else:
+            sentences = [
+                (doc["title"] + self.sep + doc["text"]).strip() if "title" in doc else doc["text"].strip()
+                for doc in corpus
+            ]
+        embeddings = self.encode(sentences, is_query=False, **kwargs)
+        return embeddings
+
+    def get_image_embeddings(self, images: list[Image.Image] | DataLoader, **kwargs):
+        return self.get_fused_embeddings(images=images, **kwargs)
+
+    def get_text_embeddings(self, texts: list[str], **kwargs):
+        return self.get_fused_embeddings(texts=texts, **kwargs)
+
+    def get_fused_embeddings(self, texts: list[str] = None, images: list[Image.Image] | DataLoader = None, **kwargs):
+        if isinstance(images, DataLoader):
+            image_loader = images
+            batch_size = image_loader.batch_size
+            image_loader.dataset.transform = None
+        else:
+            batch_size = kwargs.pop('batch_size', 32)
+            if images is None:
+                image_loader = None
+            else:
+                image_loader = DataLoader(
+                    images,
+                    batch_size=batch_size,
+                    shuffle=False,
+                    collate_fn=custom_collate_fn,
+                    num_workers=min(math.floor(os.cpu_count() / 2), 8),
+                )
+
+        if texts is None:
+            assert image_loader is not None
+            n_batch = len(image_loader)
+        else:
+            n_batch = len(texts) // batch_size + int(len(texts) % batch_size > 0)
+            image_loader = image_loader or [None] * n_batch
+
+        all_embeddings = list()
+        none_batch = [None] * batch_size
+        show_progress_bar = kwargs.pop('show_progress_bar', True)
+        pbar = tqdm(total=n_batch, disable=not show_progress_bar, mininterval=1, miniters=10, desc='encode')
+        for n, img_batch in zip(range(0, n_batch * batch_size, batch_size), image_loader):
+            text_batch = none_batch if texts is None else texts[n: n+batch_size]
+            img_batch = none_batch if img_batch is None else img_batch
+            embeddings = self.embed(texts=text_batch, images=img_batch, **kwargs)
+            pbar.update(1)
+            all_embeddings.append(embeddings.cpu())
+        pbar.close()
+        all_embeddings = torch.cat(all_embeddings, dim=0)
+        return all_embeddings
+
+
+def custom_collate_fn(batch):
+    return batch
+
+
+### Copied from qwen_vl_utils.vision_process.py
+import base64
+from io import BytesIO
+import requests
+
+IMAGE_FACTOR = 28
+MIN_PIXELS = 4 * 28 * 28
+MAX_PIXELS = 16384 * 28 * 28
+MAX_RATIO = 200
+
+
+def round_by_factor(number: int, factor: int) -> int:
+    """Returns the closest integer to 'number' that is divisible by 'factor'."""
+    return round(number / factor) * factor
+
+
+def ceil_by_factor(number: int, factor: int) -> int:
+    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
+    return math.ceil(number / factor) * factor
+
+
+def floor_by_factor(number: int, factor: int) -> int:
+    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
+    return math.floor(number / factor) * factor
+
+
+def smart_resize(
+    height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
+) -> tuple[int, int]:
+    """
+    Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+    """
+    h_bar = max(factor, round_by_factor(height, factor))
+    w_bar = max(factor, round_by_factor(width, factor))
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = floor_by_factor(height / beta, factor)
+        w_bar = floor_by_factor(width / beta, factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = ceil_by_factor(height * beta, factor)
+        w_bar = ceil_by_factor(width * beta, factor)
+
+    if max(h_bar, w_bar) / min(h_bar, w_bar) > MAX_RATIO:
+        logging.warning(
+            f"Absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(h_bar, w_bar) / min(h_bar, w_bar)}"
+        )
+        if h_bar > w_bar:
+            h_bar = w_bar * MAX_RATIO
+        else:
+            w_bar = h_bar * MAX_RATIO
+    return h_bar, w_bar
+
+
+def fetch_image(image: str | Image.Image, size_factor: int = IMAGE_FACTOR) -> Image.Image:
+    image_obj = None
+    if isinstance(image, Image.Image):
+        image_obj = image
+    elif image.startswith("http://") or image.startswith("https://"):
+        image_obj = Image.open(requests.get(image, stream=True).raw)
+    elif image.startswith("file://"):
+        image_obj = Image.open(image[7:])
+    elif image.startswith("data:image"):
+        if "base64," in image:
+            _, base64_data = image.split("base64,", 1)
+            data = base64.b64decode(base64_data)
+            image_obj = Image.open(BytesIO(data))
+    else:
+        image_obj = Image.open(image)
+    if image_obj is None:
+        raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
+    image = image_obj.convert("RGB")
+    ## resize
+    # if "resized_height" in ele and "resized_width" in ele:
+    #     resized_height, resized_width = smart_resize(
+    #         ele["resized_height"],
+    #         ele["resized_width"],
+    #         factor=size_factor,
+    #     )
+    # else:
+    width, height = image.size
+    # min_pixels = ele.get("min_pixels", MIN_PIXELS)
+    # max_pixels = ele.get("max_pixels", MAX_PIXELS)
+    resized_height, resized_width = smart_resize(
+        height,
+        width,
+        factor=size_factor,
+        min_pixels=MIN_PIXELS,
+        max_pixels=MAX_PIXELS,
+    )
+    image = image.resize((resized_width, resized_height))
+
+    return image
+###
+
+
+if __name__ == '__main__':
+    texts = [
+        "What kind of car is this?",
+        "The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023."
+    ]
+    images = [
+        'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',
+        'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
+    ]
+
+    gme = GmeQwen2VL("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct")
+
+    # Single-modal embedding
+    e_text = gme.get_text_embeddings(texts=texts)
+    e_image = gme.get_image_embeddings(images=images)
+    print((e_text * e_image).sum(-1))
+    ## tensor([0.2281, 0.6001], dtype=torch.float16)
+
+    # How to set embedding instruction
+    e_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')
+    # If is_query=False, we always use the default instruction.
+    e_corpus = gme.get_image_embeddings(images=images, is_query=False)
+    print((e_query * e_corpus).sum(-1))
+    ## tensor([0.2433, 0.7051], dtype=torch.float16)
+
+    # Fused-modal embedding
+    e_fused = gme.get_fused_embeddings(texts=texts, images=images)
+    print((e_fused[0] * e_fused[1]).sum())
+    ## tensor(0.6108, dtype=torch.float16)

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+    "visual.merger.ln_q.bias": "model-00001-of-00003.safetensors",
+    "visual.merger.ln_q.weight": "model-00001-of-00003.safetensors",
+    "visual.merger.mlp.0.bias": "model-00001-of-00003.safetensors",
+    "visual.merger.mlp.0.weight": "model-00001-of-00003.safetensors",
+    "visual.merger.mlp.2.bias": "model-00001-of-00003.safetensors",
+    "visual.merger.mlp.2.weight": "model-00001-of-00003.safetensors",
+    "visual.patch_embed.proj.weight": "model-00001-of-00003.safetensors"
+  }
+}

preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "min_pixels": 3136,
+  "max_pixels": 12845056,
+  "patch_size": 14,
+  "temporal_patch_size": 2,
+  "merge_size": 2,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "processor_class": "Qwen2VLProcessor"
+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,143 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}