Add readme

Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>

README.md

@@ -1,3 +1,2685 @@
 ---
+language:
+- en
+library_name: sentence-transformers
 license: mit
+pipeline_tag: sentence-similarity
+tags:
+  - feature-extraction
+  - mteb
+  - sentence-similarity
+  - sentence-transformers
+
+model-index:
+- name: GIST-small-Embedding-v0
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 73.40298507462688
+    - type: ap
+      value: 36.01661955459773
+    - type: f1
+      value: 67.35688942295793
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 92.71195000000002
+    - type: ap
+      value: 89.33528835459364
+    - type: f1
+      value: 92.69653287380515
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 49.007999999999996
+    - type: f1
+      value: 48.44310279702607
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 36.272999999999996
+    - type: map_at_10
+      value: 52.059999999999995
+    - type: map_at_100
+      value: 52.75300000000001
+    - type: map_at_1000
+      value: 52.756
+    - type: map_at_3
+      value: 47.57
+    - type: map_at_5
+      value: 50.236999999999995
+    - type: mrr_at_1
+      value: 36.272999999999996
+    - type: mrr_at_10
+      value: 51.942
+    - type: mrr_at_100
+      value: 52.634
+    - type: mrr_at_1000
+      value: 52.637
+    - type: mrr_at_3
+      value: 47.475
+    - type: mrr_at_5
+      value: 50.11
+    - type: ndcg_at_1
+      value: 36.272999999999996
+    - type: ndcg_at_10
+      value: 60.558
+    - type: ndcg_at_100
+      value: 63.293
+    - type: ndcg_at_1000
+      value: 63.375
+    - type: ndcg_at_3
+      value: 51.364
+    - type: ndcg_at_5
+      value: 56.154
+    - type: precision_at_1
+      value: 36.272999999999996
+    - type: precision_at_10
+      value: 8.755
+    - type: precision_at_100
+      value: 0.9900000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 20.791999999999998
+    - type: precision_at_5
+      value: 14.793999999999999
+    - type: recall_at_1
+      value: 36.272999999999996
+    - type: recall_at_10
+      value: 87.553
+    - type: recall_at_100
+      value: 99.004
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 62.376
+    - type: recall_at_5
+      value: 73.969
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.79137102109872
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 40.03049595085257
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.868157850825256
+    - type: mrr
+      value: 75.33922525612276
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.96056116438724
+    - type: cos_sim_spearman
+      value: 87.32608616965557
+    - type: euclidean_pearson
+      value: 87.40536769084146
+    - type: euclidean_spearman
+      value: 87.39235273982528
+    - type: manhattan_pearson
+      value: 87.4496043849794
+    - type: manhattan_spearman
+      value: 87.1128282983821
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 86.16883116883116
+    - type: f1
+      value: 86.1338488750026
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 38.950791675044
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 35.40686850755838
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.891000000000002
+    - type: map_at_10
+      value: 42.624
+    - type: map_at_100
+      value: 44.205
+    - type: map_at_1000
+      value: 44.336999999999996
+    - type: map_at_3
+      value: 38.81
+    - type: map_at_5
+      value: 41.152
+    - type: mrr_at_1
+      value: 38.196999999999996
+    - type: mrr_at_10
+      value: 48.641
+    - type: mrr_at_100
+      value: 49.329
+    - type: mrr_at_1000
+      value: 49.376
+    - type: mrr_at_3
+      value: 45.637
+    - type: mrr_at_5
+      value: 47.611
+    - type: ndcg_at_1
+      value: 38.196999999999996
+    - type: ndcg_at_10
+      value: 49.274
+    - type: ndcg_at_100
+      value: 54.716
+    - type: ndcg_at_1000
+      value: 56.654
+    - type: ndcg_at_3
+      value: 43.787
+    - type: ndcg_at_5
+      value: 46.719
+    - type: precision_at_1
+      value: 38.196999999999996
+    - type: precision_at_10
+      value: 9.585
+    - type: precision_at_100
+      value: 1.545
+    - type: precision_at_1000
+      value: 0.20400000000000001
+    - type: precision_at_3
+      value: 21.173000000000002
+    - type: precision_at_5
+      value: 15.536
+    - type: recall_at_1
+      value: 30.891000000000002
+    - type: recall_at_10
+      value: 61.792
+    - type: recall_at_100
+      value: 84.526
+    - type: recall_at_1000
+      value: 96.717
+    - type: recall_at_3
+      value: 46.472
+    - type: recall_at_5
+      value: 54.391999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.266
+    - type: map_at_10
+      value: 39.717999999999996
+    - type: map_at_100
+      value: 40.971000000000004
+    - type: map_at_1000
+      value: 41.097
+    - type: map_at_3
+      value: 36.858999999999995
+    - type: map_at_5
+      value: 38.405
+    - type: mrr_at_1
+      value: 37.452000000000005
+    - type: mrr_at_10
+      value: 45.528
+    - type: mrr_at_100
+      value: 46.178000000000004
+    - type: mrr_at_1000
+      value: 46.221000000000004
+    - type: mrr_at_3
+      value: 43.089
+    - type: mrr_at_5
+      value: 44.497
+    - type: ndcg_at_1
+      value: 37.452000000000005
+    - type: ndcg_at_10
+      value: 45.282
+    - type: ndcg_at_100
+      value: 49.742
+    - type: ndcg_at_1000
+      value: 51.754999999999995
+    - type: ndcg_at_3
+      value: 41.024
+    - type: ndcg_at_5
+      value: 42.912
+    - type: precision_at_1
+      value: 37.452000000000005
+    - type: precision_at_10
+      value: 8.516
+    - type: precision_at_100
+      value: 1.3679999999999999
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 19.575
+    - type: precision_at_5
+      value: 13.771
+    - type: recall_at_1
+      value: 30.266
+    - type: recall_at_10
+      value: 55.086
+    - type: recall_at_100
+      value: 74.083
+    - type: recall_at_1000
+      value: 86.722
+    - type: recall_at_3
+      value: 42.449999999999996
+    - type: recall_at_5
+      value: 47.975
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.217
+    - type: map_at_10
+      value: 51.466
+    - type: map_at_100
+      value: 52.531000000000006
+    - type: map_at_1000
+      value: 52.586
+    - type: map_at_3
+      value: 47.942
+    - type: map_at_5
+      value: 49.988
+    - type: mrr_at_1
+      value: 44.765
+    - type: mrr_at_10
+      value: 54.748
+    - type: mrr_at_100
+      value: 55.41199999999999
+    - type: mrr_at_1000
+      value: 55.437999999999995
+    - type: mrr_at_3
+      value: 52.017
+    - type: mrr_at_5
+      value: 53.693999999999996
+    - type: ndcg_at_1
+      value: 44.765
+    - type: ndcg_at_10
+      value: 57.397
+    - type: ndcg_at_100
+      value: 61.526
+    - type: ndcg_at_1000
+      value: 62.577000000000005
+    - type: ndcg_at_3
+      value: 51.414
+    - type: ndcg_at_5
+      value: 54.486999999999995
+    - type: precision_at_1
+      value: 44.765
+    - type: precision_at_10
+      value: 9.354
+    - type: precision_at_100
+      value: 1.2309999999999999
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 22.820999999999998
+    - type: precision_at_5
+      value: 16.012999999999998
+    - type: recall_at_1
+      value: 39.217
+    - type: recall_at_10
+      value: 71.588
+    - type: recall_at_100
+      value: 89.473
+    - type: recall_at_1000
+      value: 96.863
+    - type: recall_at_3
+      value: 55.943
+    - type: recall_at_5
+      value: 63.14999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.451
+    - type: map_at_10
+      value: 34.738
+    - type: map_at_100
+      value: 35.769
+    - type: map_at_1000
+      value: 35.851
+    - type: map_at_3
+      value: 32.002
+    - type: map_at_5
+      value: 33.800999999999995
+    - type: mrr_at_1
+      value: 28.814
+    - type: mrr_at_10
+      value: 36.992000000000004
+    - type: mrr_at_100
+      value: 37.901
+    - type: mrr_at_1000
+      value: 37.964
+    - type: mrr_at_3
+      value: 34.426
+    - type: mrr_at_5
+      value: 36.075
+    - type: ndcg_at_1
+      value: 28.814
+    - type: ndcg_at_10
+      value: 39.667
+    - type: ndcg_at_100
+      value: 44.741
+    - type: ndcg_at_1000
+      value: 46.763
+    - type: ndcg_at_3
+      value: 34.461999999999996
+    - type: ndcg_at_5
+      value: 37.472
+    - type: precision_at_1
+      value: 28.814
+    - type: precision_at_10
+      value: 6.045
+    - type: precision_at_100
+      value: 0.903
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 14.463000000000001
+    - type: precision_at_5
+      value: 10.418
+    - type: recall_at_1
+      value: 26.451
+    - type: recall_at_10
+      value: 52.751999999999995
+    - type: recall_at_100
+      value: 75.971
+    - type: recall_at_1000
+      value: 91.02
+    - type: recall_at_3
+      value: 38.896
+    - type: recall_at_5
+      value: 46.126
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.03
+    - type: map_at_10
+      value: 24.474999999999998
+    - type: map_at_100
+      value: 25.650000000000002
+    - type: map_at_1000
+      value: 25.764
+    - type: map_at_3
+      value: 21.656
+    - type: map_at_5
+      value: 23.269000000000002
+    - type: mrr_at_1
+      value: 20.025000000000002
+    - type: mrr_at_10
+      value: 29.325000000000003
+    - type: mrr_at_100
+      value: 30.264999999999997
+    - type: mrr_at_1000
+      value: 30.325000000000003
+    - type: mrr_at_3
+      value: 26.493
+    - type: mrr_at_5
+      value: 28.197
+    - type: ndcg_at_1
+      value: 20.025000000000002
+    - type: ndcg_at_10
+      value: 30.012
+    - type: ndcg_at_100
+      value: 35.760999999999996
+    - type: ndcg_at_1000
+      value: 38.53
+    - type: ndcg_at_3
+      value: 24.863
+    - type: ndcg_at_5
+      value: 27.36
+    - type: precision_at_1
+      value: 20.025000000000002
+    - type: precision_at_10
+      value: 5.721
+    - type: precision_at_100
+      value: 0.9809999999999999
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 12.189
+    - type: precision_at_5
+      value: 9.08
+    - type: recall_at_1
+      value: 16.03
+    - type: recall_at_10
+      value: 42.263
+    - type: recall_at_100
+      value: 67.868
+    - type: recall_at_1000
+      value: 87.77000000000001
+    - type: recall_at_3
+      value: 27.932000000000002
+    - type: recall_at_5
+      value: 34.46
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.358
+    - type: map_at_10
+      value: 39.753
+    - type: map_at_100
+      value: 41.031
+    - type: map_at_1000
+      value: 41.135
+    - type: map_at_3
+      value: 36.515
+    - type: map_at_5
+      value: 38.346999999999994
+    - type: mrr_at_1
+      value: 35.9
+    - type: mrr_at_10
+      value: 45.336
+    - type: mrr_at_100
+      value: 46.087
+    - type: mrr_at_1000
+      value: 46.129999999999995
+    - type: mrr_at_3
+      value: 42.620999999999995
+    - type: mrr_at_5
+      value: 44.224000000000004
+    - type: ndcg_at_1
+      value: 35.9
+    - type: ndcg_at_10
+      value: 45.85
+    - type: ndcg_at_100
+      value: 51.186
+    - type: ndcg_at_1000
+      value: 53.154999999999994
+    - type: ndcg_at_3
+      value: 40.594
+    - type: ndcg_at_5
+      value: 43.169999999999995
+    - type: precision_at_1
+      value: 35.9
+    - type: precision_at_10
+      value: 8.402
+    - type: precision_at_100
+      value: 1.2850000000000001
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_3
+      value: 19.249
+    - type: precision_at_5
+      value: 13.763
+    - type: recall_at_1
+      value: 29.358
+    - type: recall_at_10
+      value: 58.257000000000005
+    - type: recall_at_100
+      value: 81.22200000000001
+    - type: recall_at_1000
+      value: 94.045
+    - type: recall_at_3
+      value: 43.599
+    - type: recall_at_5
+      value: 50.232
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.954
+    - type: map_at_10
+      value: 33.767
+    - type: map_at_100
+      value: 35.225
+    - type: map_at_1000
+      value: 35.339
+    - type: map_at_3
+      value: 30.746000000000002
+    - type: map_at_5
+      value: 32.318000000000005
+    - type: mrr_at_1
+      value: 30.137000000000004
+    - type: mrr_at_10
+      value: 39.24
+    - type: mrr_at_100
+      value: 40.235
+    - type: mrr_at_1000
+      value: 40.294999999999995
+    - type: mrr_at_3
+      value: 36.758
+    - type: mrr_at_5
+      value: 38.031
+    - type: ndcg_at_1
+      value: 30.137000000000004
+    - type: ndcg_at_10
+      value: 39.711999999999996
+    - type: ndcg_at_100
+      value: 45.795
+    - type: ndcg_at_1000
+      value: 48.178
+    - type: ndcg_at_3
+      value: 34.768
+    - type: ndcg_at_5
+      value: 36.756
+    - type: precision_at_1
+      value: 30.137000000000004
+    - type: precision_at_10
+      value: 7.443
+    - type: precision_at_100
+      value: 1.221
+    - type: precision_at_1000
+      value: 0.159
+    - type: precision_at_3
+      value: 16.933
+    - type: precision_at_5
+      value: 11.918
+    - type: recall_at_1
+      value: 23.954
+    - type: recall_at_10
+      value: 52.234
+    - type: recall_at_100
+      value: 77.75800000000001
+    - type: recall_at_1000
+      value: 94.072
+    - type: recall_at_3
+      value: 37.876
+    - type: recall_at_5
+      value: 43.494
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.478416666666664
+    - type: map_at_10
+      value: 34.483999999999995
+    - type: map_at_100
+      value: 35.71641666666667
+    - type: map_at_1000
+      value: 35.8315
+    - type: map_at_3
+      value: 31.571083333333334
+    - type: map_at_5
+      value: 33.229749999999996
+    - type: mrr_at_1
+      value: 30.122416666666663
+    - type: mrr_at_10
+      value: 38.608333333333334
+    - type: mrr_at_100
+      value: 39.465500000000006
+    - type: mrr_at_1000
+      value: 39.52375
+    - type: mrr_at_3
+      value: 36.047916666666666
+    - type: mrr_at_5
+      value: 37.53833333333333
+    - type: ndcg_at_1
+      value: 30.122416666666663
+    - type: ndcg_at_10
+      value: 39.87575
+    - type: ndcg_at_100
+      value: 45.15691666666666
+    - type: ndcg_at_1000
+      value: 47.43891666666667
+    - type: ndcg_at_3
+      value: 34.88666666666666
+    - type: ndcg_at_5
+      value: 37.30966666666667
+    - type: precision_at_1
+      value: 30.122416666666663
+    - type: precision_at_10
+      value: 7.056500000000001
+    - type: precision_at_100
+      value: 1.1415000000000002
+    - type: precision_at_1000
+      value: 0.15308333333333332
+    - type: precision_at_3
+      value: 16.03525
+    - type: precision_at_5
+      value: 11.51125
+    - type: recall_at_1
+      value: 25.478416666666664
+    - type: recall_at_10
+      value: 51.72658333333333
+    - type: recall_at_100
+      value: 74.94641666666666
+    - type: recall_at_1000
+      value: 90.75300000000001
+    - type: recall_at_3
+      value: 37.93833333333333
+    - type: recall_at_5
+      value: 44.15625
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.697
+    - type: map_at_10
+      value: 30.919999999999998
+    - type: map_at_100
+      value: 31.889
+    - type: map_at_1000
+      value: 31.985000000000003
+    - type: map_at_3
+      value: 29.046
+    - type: map_at_5
+      value: 29.902
+    - type: mrr_at_1
+      value: 27.454
+    - type: mrr_at_10
+      value: 33.517
+    - type: mrr_at_100
+      value: 34.381
+    - type: mrr_at_1000
+      value: 34.452
+    - type: mrr_at_3
+      value: 31.747999999999998
+    - type: mrr_at_5
+      value: 32.561
+    - type: ndcg_at_1
+      value: 27.454
+    - type: ndcg_at_10
+      value: 34.687
+    - type: ndcg_at_100
+      value: 39.395
+    - type: ndcg_at_1000
+      value: 41.826
+    - type: ndcg_at_3
+      value: 31.102
+    - type: ndcg_at_5
+      value: 32.435
+    - type: precision_at_1
+      value: 27.454
+    - type: precision_at_10
+      value: 5.322
+    - type: precision_at_100
+      value: 0.83
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 13.088
+    - type: precision_at_5
+      value: 8.803999999999998
+    - type: recall_at_1
+      value: 24.697
+    - type: recall_at_10
+      value: 43.688
+    - type: recall_at_100
+      value: 64.893
+    - type: recall_at_1000
+      value: 82.755
+    - type: recall_at_3
+      value: 33.896
+    - type: recall_at_5
+      value: 37.174
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.525000000000002
+    - type: map_at_10
+      value: 23.435
+    - type: map_at_100
+      value: 24.535999999999998
+    - type: map_at_1000
+      value: 24.672
+    - type: map_at_3
+      value: 21.095
+    - type: map_at_5
+      value: 22.308
+    - type: mrr_at_1
+      value: 19.993
+    - type: mrr_at_10
+      value: 27.096999999999998
+    - type: mrr_at_100
+      value: 28.036
+    - type: mrr_at_1000
+      value: 28.119
+    - type: mrr_at_3
+      value: 24.971
+    - type: mrr_at_5
+      value: 26.062
+    - type: ndcg_at_1
+      value: 19.993
+    - type: ndcg_at_10
+      value: 28.002
+    - type: ndcg_at_100
+      value: 33.288000000000004
+    - type: ndcg_at_1000
+      value: 36.416
+    - type: ndcg_at_3
+      value: 23.768
+    - type: ndcg_at_5
+      value: 25.579
+    - type: precision_at_1
+      value: 19.993
+    - type: precision_at_10
+      value: 5.196
+    - type: precision_at_100
+      value: 0.922
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 11.241
+    - type: precision_at_5
+      value: 8.176
+    - type: recall_at_1
+      value: 16.525000000000002
+    - type: recall_at_10
+      value: 38.082
+    - type: recall_at_100
+      value: 61.866
+    - type: recall_at_1000
+      value: 84.20100000000001
+    - type: recall_at_3
+      value: 26.228
+    - type: recall_at_5
+      value: 30.86
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.480999999999998
+    - type: map_at_10
+      value: 34.319
+    - type: map_at_100
+      value: 35.54
+    - type: map_at_1000
+      value: 35.648
+    - type: map_at_3
+      value: 31.533
+    - type: map_at_5
+      value: 33.058
+    - type: mrr_at_1
+      value: 29.851
+    - type: mrr_at_10
+      value: 38.243
+    - type: mrr_at_100
+      value: 39.172000000000004
+    - type: mrr_at_1000
+      value: 39.235
+    - type: mrr_at_3
+      value: 35.697
+    - type: mrr_at_5
+      value: 37.147000000000006
+    - type: ndcg_at_1
+      value: 29.851
+    - type: ndcg_at_10
+      value: 39.653
+    - type: ndcg_at_100
+      value: 45.065
+    - type: ndcg_at_1000
+      value: 47.477999999999994
+    - type: ndcg_at_3
+      value: 34.481
+    - type: ndcg_at_5
+      value: 36.870999999999995
+    - type: precision_at_1
+      value: 29.851
+    - type: precision_at_10
+      value: 6.679
+    - type: precision_at_100
+      value: 1.053
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 15.485
+    - type: precision_at_5
+      value: 10.989
+    - type: recall_at_1
+      value: 25.480999999999998
+    - type: recall_at_10
+      value: 52.032000000000004
+    - type: recall_at_100
+      value: 75.193
+    - type: recall_at_1000
+      value: 91.958
+    - type: recall_at_3
+      value: 38.089
+    - type: recall_at_5
+      value: 43.947
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.148
+    - type: map_at_10
+      value: 33.007
+    - type: map_at_100
+      value: 34.602
+    - type: map_at_1000
+      value: 34.809
+    - type: map_at_3
+      value: 30.014000000000003
+    - type: map_at_5
+      value: 31.728
+    - type: mrr_at_1
+      value: 29.842000000000002
+    - type: mrr_at_10
+      value: 37.318
+    - type: mrr_at_100
+      value: 38.353
+    - type: mrr_at_1000
+      value: 38.41
+    - type: mrr_at_3
+      value: 34.75
+    - type: mrr_at_5
+      value: 36.163000000000004
+    - type: ndcg_at_1
+      value: 29.842000000000002
+    - type: ndcg_at_10
+      value: 38.462
+    - type: ndcg_at_100
+      value: 44.86
+    - type: ndcg_at_1000
+      value: 47.375
+    - type: ndcg_at_3
+      value: 33.614
+    - type: ndcg_at_5
+      value: 36.032
+    - type: precision_at_1
+      value: 29.842000000000002
+    - type: precision_at_10
+      value: 7.332
+    - type: precision_at_100
+      value: 1.52
+    - type: precision_at_1000
+      value: 0.23900000000000002
+    - type: precision_at_3
+      value: 15.547
+    - type: precision_at_5
+      value: 11.423
+    - type: recall_at_1
+      value: 25.148
+    - type: recall_at_10
+      value: 48.894
+    - type: recall_at_100
+      value: 77.845
+    - type: recall_at_1000
+      value: 93.74900000000001
+    - type: recall_at_3
+      value: 35.17
+    - type: recall_at_5
+      value: 41.734
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.723
+    - type: map_at_10
+      value: 25.586
+    - type: map_at_100
+      value: 26.648
+    - type: map_at_1000
+      value: 26.755000000000003
+    - type: map_at_3
+      value: 22.634999999999998
+    - type: map_at_5
+      value: 24.481
+    - type: mrr_at_1
+      value: 19.039
+    - type: mrr_at_10
+      value: 27.315
+    - type: mrr_at_100
+      value: 28.237000000000002
+    - type: mrr_at_1000
+      value: 28.32
+    - type: mrr_at_3
+      value: 24.368000000000002
+    - type: mrr_at_5
+      value: 26.198
+    - type: ndcg_at_1
+      value: 19.039
+    - type: ndcg_at_10
+      value: 30.511
+    - type: ndcg_at_100
+      value: 35.808
+    - type: ndcg_at_1000
+      value: 38.56
+    - type: ndcg_at_3
+      value: 24.762999999999998
+    - type: ndcg_at_5
+      value: 27.923
+    - type: precision_at_1
+      value: 19.039
+    - type: precision_at_10
+      value: 5.083
+    - type: precision_at_100
+      value: 0.839
+    - type: precision_at_1000
+      value: 0.11800000000000001
+    - type: precision_at_3
+      value: 10.659
+    - type: precision_at_5
+      value: 8.244
+    - type: recall_at_1
+      value: 17.723
+    - type: recall_at_10
+      value: 44.051
+    - type: recall_at_100
+      value: 68.659
+    - type: recall_at_1000
+      value: 89.164
+    - type: recall_at_3
+      value: 28.709
+    - type: recall_at_5
+      value: 36.331
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 13.669999999999998
+    - type: map_at_10
+      value: 23.46
+    - type: map_at_100
+      value: 25.304
+    - type: map_at_1000
+      value: 25.497999999999998
+    - type: map_at_3
+      value: 19.702
+    - type: map_at_5
+      value: 21.642
+    - type: mrr_at_1
+      value: 31.269999999999996
+    - type: mrr_at_10
+      value: 43.264
+    - type: mrr_at_100
+      value: 44.1
+    - type: mrr_at_1000
+      value: 44.134
+    - type: mrr_at_3
+      value: 40.011
+    - type: mrr_at_5
+      value: 42.079
+    - type: ndcg_at_1
+      value: 31.269999999999996
+    - type: ndcg_at_10
+      value: 32.385000000000005
+    - type: ndcg_at_100
+      value: 39.282000000000004
+    - type: ndcg_at_1000
+      value: 42.628
+    - type: ndcg_at_3
+      value: 26.942
+    - type: ndcg_at_5
+      value: 28.832
+    - type: precision_at_1
+      value: 31.269999999999996
+    - type: precision_at_10
+      value: 10.123999999999999
+    - type: precision_at_100
+      value: 1.748
+    - type: precision_at_1000
+      value: 0.23700000000000002
+    - type: precision_at_3
+      value: 20.282
+    - type: precision_at_5
+      value: 15.479000000000001
+    - type: recall_at_1
+      value: 13.669999999999998
+    - type: recall_at_10
+      value: 38.078
+    - type: recall_at_100
+      value: 61.651
+    - type: recall_at_1000
+      value: 80.279
+    - type: recall_at_3
+      value: 24.438
+    - type: recall_at_5
+      value: 30.244
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.103
+    - type: map_at_10
+      value: 19.238
+    - type: map_at_100
+      value: 26.451999999999998
+    - type: map_at_1000
+      value: 27.987000000000002
+    - type: map_at_3
+      value: 14.069999999999999
+    - type: map_at_5
+      value: 16.434
+    - type: mrr_at_1
+      value: 67.5
+    - type: mrr_at_10
+      value: 75.64800000000001
+    - type: mrr_at_100
+      value: 75.847
+    - type: mrr_at_1000
+      value: 75.85499999999999
+    - type: mrr_at_3
+      value: 73.833
+    - type: mrr_at_5
+      value: 74.933
+    - type: ndcg_at_1
+      value: 55.625
+    - type: ndcg_at_10
+      value: 40.505
+    - type: ndcg_at_100
+      value: 44.505
+    - type: ndcg_at_1000
+      value: 52.005
+    - type: ndcg_at_3
+      value: 45.841
+    - type: ndcg_at_5
+      value: 42.945
+    - type: precision_at_1
+      value: 67.5
+    - type: precision_at_10
+      value: 31.6
+    - type: precision_at_100
+      value: 9.83
+    - type: precision_at_1000
+      value: 1.9619999999999997
+    - type: precision_at_3
+      value: 49.083
+    - type: precision_at_5
+      value: 41.15
+    - type: recall_at_1
+      value: 9.103
+    - type: recall_at_10
+      value: 24.6
+    - type: recall_at_100
+      value: 50.075
+    - type: recall_at_1000
+      value: 73.516
+    - type: recall_at_3
+      value: 15.35
+    - type: recall_at_5
+      value: 19.217000000000002
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 50.595
+    - type: f1
+      value: 45.43005573726517
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 76.08200000000001
+    - type: map_at_10
+      value: 83.697
+    - type: map_at_100
+      value: 83.891
+    - type: map_at_1000
+      value: 83.905
+    - type: map_at_3
+      value: 82.69
+    - type: map_at_5
+      value: 83.35900000000001
+    - type: mrr_at_1
+      value: 82.148
+    - type: mrr_at_10
+      value: 88.727
+    - type: mrr_at_100
+      value: 88.787
+    - type: mrr_at_1000
+      value: 88.788
+    - type: mrr_at_3
+      value: 88.054
+    - type: mrr_at_5
+      value: 88.547
+    - type: ndcg_at_1
+      value: 82.148
+    - type: ndcg_at_10
+      value: 87.274
+    - type: ndcg_at_100
+      value: 87.957
+    - type: ndcg_at_1000
+      value: 88.203
+    - type: ndcg_at_3
+      value: 85.744
+    - type: ndcg_at_5
+      value: 86.664
+    - type: precision_at_1
+      value: 82.148
+    - type: precision_at_10
+      value: 10.315000000000001
+    - type: precision_at_100
+      value: 1.086
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 32.458
+    - type: precision_at_5
+      value: 20.09
+    - type: recall_at_1
+      value: 76.08200000000001
+    - type: recall_at_10
+      value: 93.408
+    - type: recall_at_100
+      value: 96.11
+    - type: recall_at_1000
+      value: 97.626
+    - type: recall_at_3
+      value: 89.172
+    - type: recall_at_5
+      value: 91.604
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.377
+    - type: map_at_10
+      value: 31.785000000000004
+    - type: map_at_100
+      value: 33.511
+    - type: map_at_1000
+      value: 33.713
+    - type: map_at_3
+      value: 27.811999999999998
+    - type: map_at_5
+      value: 30.148000000000003
+    - type: mrr_at_1
+      value: 38.426
+    - type: mrr_at_10
+      value: 47.233000000000004
+    - type: mrr_at_100
+      value: 47.980000000000004
+    - type: mrr_at_1000
+      value: 48.022
+    - type: mrr_at_3
+      value: 44.856
+    - type: mrr_at_5
+      value: 46.322
+    - type: ndcg_at_1
+      value: 38.426
+    - type: ndcg_at_10
+      value: 39.326
+    - type: ndcg_at_100
+      value: 45.769999999999996
+    - type: ndcg_at_1000
+      value: 49.131
+    - type: ndcg_at_3
+      value: 36.1
+    - type: ndcg_at_5
+      value: 37.271
+    - type: precision_at_1
+      value: 38.426
+    - type: precision_at_10
+      value: 11.126999999999999
+    - type: precision_at_100
+      value: 1.7870000000000001
+    - type: precision_at_1000
+      value: 0.23700000000000002
+    - type: precision_at_3
+      value: 24.587999999999997
+    - type: precision_at_5
+      value: 18.21
+    - type: recall_at_1
+      value: 19.377
+    - type: recall_at_10
+      value: 45.484
+    - type: recall_at_100
+      value: 69.968
+    - type: recall_at_1000
+      value: 90.30799999999999
+    - type: recall_at_3
+      value: 32.72
+    - type: recall_at_5
+      value: 38.856
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 37.475
+    - type: map_at_10
+      value: 58.662000000000006
+    - type: map_at_100
+      value: 59.561
+    - type: map_at_1000
+      value: 59.626999999999995
+    - type: map_at_3
+      value: 55.496
+    - type: map_at_5
+      value: 57.464000000000006
+    - type: mrr_at_1
+      value: 74.949
+    - type: mrr_at_10
+      value: 80.976
+    - type: mrr_at_100
+      value: 81.215
+    - type: mrr_at_1000
+      value: 81.22399999999999
+    - type: mrr_at_3
+      value: 79.892
+    - type: mrr_at_5
+      value: 80.57
+    - type: ndcg_at_1
+      value: 74.949
+    - type: ndcg_at_10
+      value: 66.93599999999999
+    - type: ndcg_at_100
+      value: 70.137
+    - type: ndcg_at_1000
+      value: 71.452
+    - type: ndcg_at_3
+      value: 62.319
+    - type: ndcg_at_5
+      value: 64.866
+    - type: precision_at_1
+      value: 74.949
+    - type: precision_at_10
+      value: 13.988999999999999
+    - type: precision_at_100
+      value: 1.6500000000000001
+    - type: precision_at_1000
+      value: 0.182
+    - type: precision_at_3
+      value: 39.806000000000004
+    - type: precision_at_5
+      value: 25.899
+    - type: recall_at_1
+      value: 37.475
+    - type: recall_at_10
+      value: 69.946
+    - type: recall_at_100
+      value: 82.478
+    - type: recall_at_1000
+      value: 91.202
+    - type: recall_at_3
+      value: 59.709999999999994
+    - type: recall_at_5
+      value: 64.747
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 89.2272
+    - type: ap
+      value: 84.69017509523854
+    - type: f1
+      value: 89.20673182133066
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.471999999999998
+    - type: map_at_10
+      value: 33.287
+    - type: map_at_100
+      value: 34.486
+    - type: map_at_1000
+      value: 34.536
+    - type: map_at_3
+      value: 29.520999999999997
+    - type: map_at_5
+      value: 31.647
+    - type: mrr_at_1
+      value: 22.076999999999998
+    - type: mrr_at_10
+      value: 33.902
+    - type: mrr_at_100
+      value: 35.037
+    - type: mrr_at_1000
+      value: 35.081
+    - type: mrr_at_3
+      value: 30.174
+    - type: mrr_at_5
+      value: 32.302
+    - type: ndcg_at_1
+      value: 22.092
+    - type: ndcg_at_10
+      value: 40.073
+    - type: ndcg_at_100
+      value: 45.82
+    - type: ndcg_at_1000
+      value: 47.097
+    - type: ndcg_at_3
+      value: 32.364
+    - type: ndcg_at_5
+      value: 36.179
+    - type: precision_at_1
+      value: 22.092
+    - type: precision_at_10
+      value: 6.36
+    - type: precision_at_100
+      value: 0.924
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 13.806
+    - type: precision_at_5
+      value: 10.223
+    - type: recall_at_1
+      value: 21.471999999999998
+    - type: recall_at_10
+      value: 60.971
+    - type: recall_at_100
+      value: 87.518
+    - type: recall_at_1000
+      value: 97.333
+    - type: recall_at_3
+      value: 39.961999999999996
+    - type: recall_at_5
+      value: 49.126
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 94.44596443228454
+    - type: f1
+      value: 94.19326360848854
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 75.7934336525308
+    - type: f1
+      value: 57.619082395865604
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 74.70410221923336
+    - type: f1
+      value: 72.82854233810865
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.61802286482852
+    - type: f1
+      value: 78.76695988384789
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 34.212621347614174
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.899728392028948
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.190245086632466
+    - type: mrr
+      value: 33.424442963159876
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.141
+    - type: map_at_10
+      value: 13.558
+    - type: map_at_100
+      value: 17.238
+    - type: map_at_1000
+      value: 18.727
+    - type: map_at_3
+      value: 9.803
+    - type: map_at_5
+      value: 11.517
+    - type: mrr_at_1
+      value: 46.129999999999995
+    - type: mrr_at_10
+      value: 54.876999999999995
+    - type: mrr_at_100
+      value: 55.428999999999995
+    - type: mrr_at_1000
+      value: 55.47
+    - type: mrr_at_3
+      value: 52.993
+    - type: mrr_at_5
+      value: 54.107000000000006
+    - type: ndcg_at_1
+      value: 43.963
+    - type: ndcg_at_10
+      value: 35.72
+    - type: ndcg_at_100
+      value: 32.792
+    - type: ndcg_at_1000
+      value: 41.52
+    - type: ndcg_at_3
+      value: 40.929
+    - type: ndcg_at_5
+      value: 38.664
+    - type: precision_at_1
+      value: 45.82
+    - type: precision_at_10
+      value: 26.625
+    - type: precision_at_100
+      value: 8.387
+    - type: precision_at_1000
+      value: 2.131
+    - type: precision_at_3
+      value: 38.39
+    - type: precision_at_5
+      value: 33.56
+    - type: recall_at_1
+      value: 6.141
+    - type: recall_at_10
+      value: 17.598
+    - type: recall_at_100
+      value: 33.619
+    - type: recall_at_1000
+      value: 64.455
+    - type: recall_at_3
+      value: 10.667
+    - type: recall_at_5
+      value: 13.492999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.019
+    - type: map_at_10
+      value: 40.644999999999996
+    - type: map_at_100
+      value: 41.870000000000005
+    - type: map_at_1000
+      value: 41.904
+    - type: map_at_3
+      value: 36.28
+    - type: map_at_5
+      value: 38.830999999999996
+    - type: mrr_at_1
+      value: 29.664
+    - type: mrr_at_10
+      value: 43.168
+    - type: mrr_at_100
+      value: 44.126
+    - type: mrr_at_1000
+      value: 44.151
+    - type: mrr_at_3
+      value: 39.484
+    - type: mrr_at_5
+      value: 41.702
+    - type: ndcg_at_1
+      value: 29.635
+    - type: ndcg_at_10
+      value: 48.284
+    - type: ndcg_at_100
+      value: 53.522999999999996
+    - type: ndcg_at_1000
+      value: 54.344
+    - type: ndcg_at_3
+      value: 40.048
+    - type: ndcg_at_5
+      value: 44.329
+    - type: precision_at_1
+      value: 29.635
+    - type: precision_at_10
+      value: 8.262
+    - type: precision_at_100
+      value: 1.1159999999999999
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 18.54
+    - type: precision_at_5
+      value: 13.586
+    - type: recall_at_1
+      value: 26.019
+    - type: recall_at_10
+      value: 69.049
+    - type: recall_at_100
+      value: 91.89399999999999
+    - type: recall_at_1000
+      value: 98.095
+    - type: recall_at_3
+      value: 47.81
+    - type: recall_at_5
+      value: 57.645
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.952
+    - type: map_at_10
+      value: 84.895
+    - type: map_at_100
+      value: 85.51299999999999
+    - type: map_at_1000
+      value: 85.529
+    - type: map_at_3
+      value: 81.94500000000001
+    - type: map_at_5
+      value: 83.83500000000001
+    - type: mrr_at_1
+      value: 81.65
+    - type: mrr_at_10
+      value: 87.756
+    - type: mrr_at_100
+      value: 87.855
+    - type: mrr_at_1000
+      value: 87.856
+    - type: mrr_at_3
+      value: 86.822
+    - type: mrr_at_5
+      value: 87.473
+    - type: ndcg_at_1
+      value: 81.65
+    - type: ndcg_at_10
+      value: 88.563
+    - type: ndcg_at_100
+      value: 89.74499999999999
+    - type: ndcg_at_1000
+      value: 89.84400000000001
+    - type: ndcg_at_3
+      value: 85.782
+    - type: ndcg_at_5
+      value: 87.381
+    - type: precision_at_1
+      value: 81.65
+    - type: precision_at_10
+      value: 13.435
+    - type: precision_at_100
+      value: 1.529
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.523
+    - type: precision_at_5
+      value: 24.72
+    - type: recall_at_1
+      value: 70.952
+    - type: recall_at_10
+      value: 95.521
+    - type: recall_at_100
+      value: 99.53699999999999
+    - type: recall_at_1000
+      value: 99.983
+    - type: recall_at_3
+      value: 87.559
+    - type: recall_at_5
+      value: 92.038
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 54.61973943122806
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 60.92179806944469
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.993
+    - type: map_at_10
+      value: 13.175999999999998
+    - type: map_at_100
+      value: 15.689
+    - type: map_at_1000
+      value: 16.054
+    - type: map_at_3
+      value: 9.325999999999999
+    - type: map_at_5
+      value: 11.283
+    - type: mrr_at_1
+      value: 24.7
+    - type: mrr_at_10
+      value: 36.568
+    - type: mrr_at_100
+      value: 37.667
+    - type: mrr_at_1000
+      value: 37.714
+    - type: mrr_at_3
+      value: 32.933
+    - type: mrr_at_5
+      value: 34.963
+    - type: ndcg_at_1
+      value: 24.7
+    - type: ndcg_at_10
+      value: 21.839
+    - type: ndcg_at_100
+      value: 31.057000000000002
+    - type: ndcg_at_1000
+      value: 36.962
+    - type: ndcg_at_3
+      value: 20.623
+    - type: ndcg_at_5
+      value: 18.107
+    - type: precision_at_1
+      value: 24.7
+    - type: precision_at_10
+      value: 11.360000000000001
+    - type: precision_at_100
+      value: 2.4619999999999997
+    - type: precision_at_1000
+      value: 0.388
+    - type: precision_at_3
+      value: 19.267
+    - type: precision_at_5
+      value: 15.959999999999999
+    - type: recall_at_1
+      value: 4.993
+    - type: recall_at_10
+      value: 22.982
+    - type: recall_at_100
+      value: 49.97
+    - type: recall_at_1000
+      value: 78.623
+    - type: recall_at_3
+      value: 11.716999999999999
+    - type: recall_at_5
+      value: 16.172
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.71899431421795
+    - type: cos_sim_spearman
+      value: 80.46430776062674
+    - type: euclidean_pearson
+      value: 83.02871101280735
+    - type: euclidean_spearman
+      value: 80.49525009964952
+    - type: manhattan_pearson
+      value: 82.96176477360466
+    - type: manhattan_spearman
+      value: 80.4038922852272
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.4473643076464
+    - type: cos_sim_spearman
+      value: 76.2648833265373
+    - type: euclidean_pearson
+      value: 82.5498605585181
+    - type: euclidean_spearman
+      value: 76.06458177068038
+    - type: manhattan_pearson
+      value: 82.55572570767087
+    - type: manhattan_spearman
+      value: 76.1267237133785
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.24858438337428
+    - type: cos_sim_spearman
+      value: 86.42907705680409
+    - type: euclidean_pearson
+      value: 85.50673274898077
+    - type: euclidean_spearman
+      value: 86.50066760759493
+    - type: manhattan_pearson
+      value: 85.38098024332331
+    - type: manhattan_spearman
+      value: 86.3179935859058
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.97052112858252
+    - type: cos_sim_spearman
+      value: 82.97007079944963
+    - type: euclidean_pearson
+      value: 84.49118913390151
+    - type: euclidean_spearman
+      value: 82.89912124589944
+    - type: manhattan_pearson
+      value: 84.45725470158602
+    - type: manhattan_spearman
+      value: 82.89422444440467
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.44702160696032
+    - type: cos_sim_spearman
+      value: 88.75678661413305
+    - type: euclidean_pearson
+      value: 88.22046240533754
+    - type: euclidean_spearman
+      value: 88.78103010580752
+    - type: manhattan_pearson
+      value: 88.15576644132916
+    - type: manhattan_spearman
+      value: 88.72891963379698
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.25112584874732
+    - type: cos_sim_spearman
+      value: 85.0642487018319
+    - type: euclidean_pearson
+      value: 84.37279427321502
+    - type: euclidean_spearman
+      value: 85.074198902509
+    - type: manhattan_pearson
+      value: 84.19323050597049
+    - type: manhattan_spearman
+      value: 84.88383717319327
+  - 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: 88.87357291874198
+    - type: cos_sim_spearman
+      value: 89.1113081854716
+    - type: euclidean_pearson
+      value: 89.61137598923361
+    - type: euclidean_spearman
+      value: 89.13391070267475
+    - type: manhattan_pearson
+      value: 89.62382071829829
+    - type: manhattan_spearman
+      value: 89.1997962715288
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 67.1205707180893
+    - type: cos_sim_spearman
+      value: 68.16260851835224
+    - type: euclidean_pearson
+      value: 68.87294373141141
+    - type: euclidean_spearman
+      value: 67.98447223948163
+    - type: manhattan_pearson
+      value: 68.98950941915248
+    - type: manhattan_spearman
+      value: 68.29388343776796
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.9949201588004
+    - type: cos_sim_spearman
+      value: 87.31663820432567
+    - type: euclidean_pearson
+      value: 87.27979534770259
+    - type: euclidean_spearman
+      value: 87.31872069375427
+    - type: manhattan_pearson
+      value: 87.0783256942344
+    - type: manhattan_spearman
+      value: 87.16038562428714
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 86.08173708317305
+    - type: mrr
+      value: 95.93575179359493
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 57.65
+    - type: map_at_10
+      value: 67.19000000000001
+    - type: map_at_100
+      value: 67.772
+    - type: map_at_1000
+      value: 67.805
+    - type: map_at_3
+      value: 64.14800000000001
+    - type: map_at_5
+      value: 65.745
+    - type: mrr_at_1
+      value: 60.333000000000006
+    - type: mrr_at_10
+      value: 68.158
+    - type: mrr_at_100
+      value: 68.583
+    - type: mrr_at_1000
+      value: 68.613
+    - type: mrr_at_3
+      value: 65.72200000000001
+    - type: mrr_at_5
+      value: 67.039
+    - type: ndcg_at_1
+      value: 60.333000000000006
+    - type: ndcg_at_10
+      value: 71.69200000000001
+    - type: ndcg_at_100
+      value: 74.064
+    - type: ndcg_at_1000
+      value: 74.694
+    - type: ndcg_at_3
+      value: 66.378
+    - type: ndcg_at_5
+      value: 68.73
+    - type: precision_at_1
+      value: 60.333000000000006
+    - type: precision_at_10
+      value: 9.533
+    - type: precision_at_100
+      value: 1.08
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 25.556
+    - type: precision_at_5
+      value: 17.0
+    - type: recall_at_1
+      value: 57.65
+    - type: recall_at_10
+      value: 84.56700000000001
+    - type: recall_at_100
+      value: 95.167
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 70.272
+    - type: recall_at_5
+      value: 76.11099999999999
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.83663366336634
+    - type: cos_sim_ap
+      value: 96.13854487816917
+    - type: cos_sim_f1
+      value: 91.77057356608479
+    - type: cos_sim_precision
+      value: 91.54228855721394
+    - type: cos_sim_recall
+      value: 92.0
+    - type: dot_accuracy
+      value: 99.83663366336634
+    - type: dot_ap
+      value: 96.29459284844314
+    - type: dot_f1
+      value: 91.6030534351145
+    - type: dot_precision
+      value: 93.26424870466322
+    - type: dot_recall
+      value: 90.0
+    - type: euclidean_accuracy
+      value: 99.83564356435643
+    - type: euclidean_ap
+      value: 96.09957152523418
+    - type: euclidean_f1
+      value: 91.7
+    - type: euclidean_precision
+      value: 91.7
+    - type: euclidean_recall
+      value: 91.7
+    - type: manhattan_accuracy
+      value: 99.83663366336634
+    - type: manhattan_ap
+      value: 96.09579952373399
+    - type: manhattan_f1
+      value: 91.72932330827068
+    - type: manhattan_precision
+      value: 91.95979899497488
+    - type: manhattan_recall
+      value: 91.5
+    - type: max_accuracy
+      value: 99.83663366336634
+    - type: max_ap
+      value: 96.29459284844314
+    - type: max_f1
+      value: 91.77057356608479
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 61.270213664772385
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.23973443659002
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.40061413824656
+    - type: mrr
+      value: 54.28819444444445
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.59314409717665
+    - type: cos_sim_spearman
+      value: 30.573109955748677
+    - type: dot_pearson
+      value: 30.884662900409722
+    - type: dot_spearman
+      value: 30.778591618272262
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.20400000000000001
+    - type: map_at_10
+      value: 1.7229999999999999
+    - type: map_at_100
+      value: 9.185
+    - type: map_at_1000
+      value: 23.019000000000002
+    - type: map_at_3
+      value: 0.596
+    - type: map_at_5
+      value: 0.9339999999999999
+    - type: mrr_at_1
+      value: 78.0
+    - type: mrr_at_10
+      value: 85.5
+    - type: mrr_at_100
+      value: 85.682
+    - type: mrr_at_1000
+      value: 85.682
+    - type: mrr_at_3
+      value: 84.0
+    - type: mrr_at_5
+      value: 85.5
+    - type: ndcg_at_1
+      value: 73.0
+    - type: ndcg_at_10
+      value: 68.28
+    - type: ndcg_at_100
+      value: 52.239000000000004
+    - type: ndcg_at_1000
+      value: 48.217
+    - type: ndcg_at_3
+      value: 72.603
+    - type: ndcg_at_5
+      value: 70.64099999999999
+    - type: precision_at_1
+      value: 78.0
+    - type: precision_at_10
+      value: 72.39999999999999
+    - type: precision_at_100
+      value: 53.459999999999994
+    - type: precision_at_1000
+      value: 21.254
+    - type: precision_at_3
+      value: 78.0
+    - type: precision_at_5
+      value: 74.8
+    - type: recall_at_1
+      value: 0.20400000000000001
+    - type: recall_at_10
+      value: 1.939
+    - type: recall_at_100
+      value: 12.831000000000001
+    - type: recall_at_1000
+      value: 45.572
+    - type: recall_at_3
+      value: 0.628
+    - type: recall_at_5
+      value: 1.004
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.693
+    - type: map_at_10
+      value: 7.7410000000000005
+    - type: map_at_100
+      value: 13.778000000000002
+    - type: map_at_1000
+      value: 15.328
+    - type: map_at_3
+      value: 4.361000000000001
+    - type: map_at_5
+      value: 5.534
+    - type: mrr_at_1
+      value: 20.408
+    - type: mrr_at_10
+      value: 37.008
+    - type: mrr_at_100
+      value: 38.198
+    - type: mrr_at_1000
+      value: 38.216
+    - type: mrr_at_3
+      value: 32.993
+    - type: mrr_at_5
+      value: 34.83
+    - type: ndcg_at_1
+      value: 18.367
+    - type: ndcg_at_10
+      value: 19.676
+    - type: ndcg_at_100
+      value: 33.421
+    - type: ndcg_at_1000
+      value: 45.123999999999995
+    - type: ndcg_at_3
+      value: 22.109
+    - type: ndcg_at_5
+      value: 20.166999999999998
+    - type: precision_at_1
+      value: 20.408
+    - type: precision_at_10
+      value: 17.551
+    - type: precision_at_100
+      value: 7.286
+    - type: precision_at_1000
+      value: 1.516
+    - type: precision_at_3
+      value: 23.810000000000002
+    - type: precision_at_5
+      value: 20.408
+    - type: recall_at_1
+      value: 1.693
+    - type: recall_at_10
+      value: 13.485
+    - type: recall_at_100
+      value: 46.361000000000004
+    - type: recall_at_1000
+      value: 81.997
+    - type: recall_at_3
+      value: 5.432
+    - type: recall_at_5
+      value: 7.797
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.6774
+    - type: ap
+      value: 14.243691983984998
+    - type: f1
+      value: 54.45105895755751
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 60.0509337860781
+    - type: f1
+      value: 60.424197644605236
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 49.94452711339773
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.75430649102938
+    - type: cos_sim_ap
+      value: 73.38576407567363
+    - type: cos_sim_f1
+      value: 67.47549019607844
+    - type: cos_sim_precision
+      value: 62.99771167048055
+    - type: cos_sim_recall
+      value: 72.63852242744063
+    - type: dot_accuracy
+      value: 85.67681945520653
+    - type: dot_ap
+      value: 73.37650773516077
+    - type: dot_f1
+      value: 67.56520653937352
+    - type: dot_precision
+      value: 64.1013497513616
+    - type: dot_recall
+      value: 71.42480211081794
+    - type: euclidean_accuracy
+      value: 85.76622757346367
+    - type: euclidean_ap
+      value: 73.31834510956003
+    - type: euclidean_f1
+      value: 67.40331491712708
+    - type: euclidean_precision
+      value: 60.780156879372484
+    - type: euclidean_recall
+      value: 75.64643799472296
+    - type: manhattan_accuracy
+      value: 85.73046432616081
+    - type: manhattan_ap
+      value: 73.10120518588954
+    - type: manhattan_f1
+      value: 67.34183545886471
+    - type: manhattan_precision
+      value: 63.997148288973385
+    - type: manhattan_recall
+      value: 71.05540897097626
+    - type: max_accuracy
+      value: 85.76622757346367
+    - type: max_ap
+      value: 73.38576407567363
+    - type: max_f1
+      value: 67.56520653937352
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.71424690495596
+    - type: cos_sim_ap
+      value: 85.42819672981983
+    - type: cos_sim_f1
+      value: 77.76150014649868
+    - type: cos_sim_precision
+      value: 74.15479184129646
+    - type: cos_sim_recall
+      value: 81.73698798891284
+    - type: dot_accuracy
+      value: 88.45810532852097
+    - type: dot_ap
+      value: 84.78667227857513
+    - type: dot_f1
+      value: 77.29539996305192
+    - type: dot_precision
+      value: 74.30560488740498
+    - type: dot_recall
+      value: 80.53587927317524
+    - type: euclidean_accuracy
+      value: 88.73171110334924
+    - type: euclidean_ap
+      value: 85.46052151213301
+    - type: euclidean_f1
+      value: 77.79939075861563
+    - type: euclidean_precision
+      value: 74.33200084157374
+    - type: euclidean_recall
+      value: 81.60609793655682
+    - type: manhattan_accuracy
+      value: 88.75111576823068
+    - type: manhattan_ap
+      value: 85.4412901701619
+    - type: manhattan_f1
+      value: 77.72423325488437
+    - type: manhattan_precision
+      value: 75.48799071184965
+    - type: manhattan_recall
+      value: 80.09701262704034
+    - type: max_accuracy
+      value: 88.75111576823068
+    - type: max_ap
+      value: 85.46052151213301
+    - type: max_f1
+      value: 77.79939075861563
 ---
+<h1 align="center">GIST small Embedding v0</h1>
+
+*GIST Embedding: Guided In-sample Selection of Training Negatives for Text Embedding*
+
+The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
+
+The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
+
+Technical details of the model will be published shortly.
+
+# Data
+
+The dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
+
+- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
+- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
+
+The dataset contains a `task_type` key which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
+
+The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
+
+The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
+
+The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis.
+
+# Usage
+
+The model can be easily loaded using the Sentence Transformers library.
+
+```Python
+import torch.nn.functional as F
+from sentence_transformers import SentenceTransformer
+
+revision = None  # Replace with the specific revision to ensure reproducibility in  case the model is updated.
+
+model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision)
+
+texts = [
+    "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
+    "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
+    "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
+]
+
+# Compute embeddings
+embeddings = model.encode(texts, convert_to_tensor=True)
+
+# Compute cosine-similarity for each pair of sentences
+scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
+
+print(scores.cpu().numpy())
+```
+
+# Training Parameters
+
+Below are the training parameters used to fine-tune the model:
+
+```
+Epochs = 40
+Warmup ratio = 0.1
+Learning rate = 5e-6
+Batch size = 16
+Checkpoint step = 102000
+Contrastive loss temperature = 0.01
+```
+
+Specific training details and strategies will be published shortly.
+
+# Evaluation
+
+The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
+
+
+# Acknowledgements
+
+This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
+
+The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.