|
--- |
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language: |
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- en |
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library_name: sentence-transformers |
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license: mit |
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pipeline_tag: sentence-similarity |
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tags: |
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- feature-extraction |
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- mteb |
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- sentence-similarity |
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- sentence-transformers |
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|
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model-index: |
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- name: GIST-large-Embedding-v0 |
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results: |
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- task: |
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type: Classification |
|
dataset: |
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type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
|
- type: accuracy |
|
value: 75.5820895522388 |
|
- type: ap |
|
value: 38.32190121241783 |
|
- type: f1 |
|
value: 69.44777155231054 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
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name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 93.40514999999998 |
|
- type: ap |
|
value: 90.2011565132406 |
|
- type: f1 |
|
value: 93.39486246843605 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (en) |
|
config: en |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
|
- type: accuracy |
|
value: 49.05999999999999 |
|
- type: f1 |
|
value: 48.58702718571088 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
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name: MTEB ArguAna |
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config: default |
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split: test |
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revision: None |
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metrics: |
|
- type: map_at_1 |
|
value: 38.407000000000004 |
|
- type: map_at_10 |
|
value: 54.822 |
|
- type: map_at_100 |
|
value: 55.387 |
|
- type: map_at_1000 |
|
value: 55.388999999999996 |
|
- type: map_at_3 |
|
value: 50.308 |
|
- type: map_at_5 |
|
value: 53.199 |
|
- type: mrr_at_1 |
|
value: 39.900000000000006 |
|
- type: mrr_at_10 |
|
value: 55.385 |
|
- type: mrr_at_100 |
|
value: 55.936 |
|
- type: mrr_at_1000 |
|
value: 55.93900000000001 |
|
- type: mrr_at_3 |
|
value: 50.853 |
|
- type: mrr_at_5 |
|
value: 53.738 |
|
- type: ndcg_at_1 |
|
value: 38.407000000000004 |
|
- type: ndcg_at_10 |
|
value: 63.38 |
|
- type: ndcg_at_100 |
|
value: 65.52900000000001 |
|
- type: ndcg_at_1000 |
|
value: 65.58800000000001 |
|
- type: ndcg_at_3 |
|
value: 54.26 |
|
- type: ndcg_at_5 |
|
value: 59.488 |
|
- type: precision_at_1 |
|
value: 38.407000000000004 |
|
- type: precision_at_10 |
|
value: 9.04 |
|
- type: precision_at_100 |
|
value: 0.992 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 21.906 |
|
- type: precision_at_5 |
|
value: 15.690000000000001 |
|
- type: recall_at_1 |
|
value: 38.407000000000004 |
|
- type: recall_at_10 |
|
value: 90.398 |
|
- type: recall_at_100 |
|
value: 99.21799999999999 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 65.718 |
|
- type: recall_at_5 |
|
value: 78.45 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 48.49766333679089 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
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split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
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metrics: |
|
- type: v_measure |
|
value: 42.57731111438094 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
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name: MTEB AskUbuntuDupQuestions |
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config: default |
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split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
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metrics: |
|
- type: map |
|
value: 64.70120072857361 |
|
- type: mrr |
|
value: 77.86714593501297 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
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split: test |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 90.73821860690765 |
|
- type: cos_sim_spearman |
|
value: 89.17070651383446 |
|
- type: euclidean_pearson |
|
value: 88.28303958293029 |
|
- type: euclidean_spearman |
|
value: 88.81889126856979 |
|
- type: manhattan_pearson |
|
value: 88.09080621828731 |
|
- type: manhattan_spearman |
|
value: 88.55924679817751 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
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split: test |
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
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metrics: |
|
- type: accuracy |
|
value: 88.10064935064933 |
|
- type: f1 |
|
value: 88.08460758973867 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
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config: default |
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split: test |
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 39.338228337929976 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
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config: default |
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split: test |
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 36.179156232378226 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
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split: test |
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revision: None |
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metrics: |
|
- type: map_at_1 |
|
value: 33.440999999999995 |
|
- type: map_at_10 |
|
value: 45.495000000000005 |
|
- type: map_at_100 |
|
value: 47.132000000000005 |
|
- type: map_at_1000 |
|
value: 47.253 |
|
- type: map_at_3 |
|
value: 41.766 |
|
- type: map_at_5 |
|
value: 43.873 |
|
- type: mrr_at_1 |
|
value: 40.772999999999996 |
|
- type: mrr_at_10 |
|
value: 51.627 |
|
- type: mrr_at_100 |
|
value: 52.364 |
|
- type: mrr_at_1000 |
|
value: 52.397000000000006 |
|
- type: mrr_at_3 |
|
value: 48.951 |
|
- type: mrr_at_5 |
|
value: 50.746 |
|
- type: ndcg_at_1 |
|
value: 40.772999999999996 |
|
- type: ndcg_at_10 |
|
value: 52.306 |
|
- type: ndcg_at_100 |
|
value: 57.753 |
|
- type: ndcg_at_1000 |
|
value: 59.36900000000001 |
|
- type: ndcg_at_3 |
|
value: 47.177 |
|
- type: ndcg_at_5 |
|
value: 49.71 |
|
- type: precision_at_1 |
|
value: 40.772999999999996 |
|
- type: precision_at_10 |
|
value: 10.129000000000001 |
|
- type: precision_at_100 |
|
value: 1.617 |
|
- type: precision_at_1000 |
|
value: 0.208 |
|
- type: precision_at_3 |
|
value: 22.985 |
|
- type: precision_at_5 |
|
value: 16.652 |
|
- type: recall_at_1 |
|
value: 33.440999999999995 |
|
- type: recall_at_10 |
|
value: 65.121 |
|
- type: recall_at_100 |
|
value: 87.55199999999999 |
|
- type: recall_at_1000 |
|
value: 97.41300000000001 |
|
- type: recall_at_3 |
|
value: 49.958999999999996 |
|
- type: recall_at_5 |
|
value: 57.14900000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.126 |
|
- type: map_at_10 |
|
value: 42.856 |
|
- type: map_at_100 |
|
value: 44.134 |
|
- type: map_at_1000 |
|
value: 44.274 |
|
- type: map_at_3 |
|
value: 39.594 |
|
- type: map_at_5 |
|
value: 41.504999999999995 |
|
- type: mrr_at_1 |
|
value: 40.127 |
|
- type: mrr_at_10 |
|
value: 48.736000000000004 |
|
- type: mrr_at_100 |
|
value: 49.303999999999995 |
|
- type: mrr_at_1000 |
|
value: 49.356 |
|
- type: mrr_at_3 |
|
value: 46.263 |
|
- type: mrr_at_5 |
|
value: 47.878 |
|
- type: ndcg_at_1 |
|
value: 40.127 |
|
- type: ndcg_at_10 |
|
value: 48.695 |
|
- type: ndcg_at_100 |
|
value: 52.846000000000004 |
|
- type: ndcg_at_1000 |
|
value: 54.964 |
|
- type: ndcg_at_3 |
|
value: 44.275 |
|
- type: ndcg_at_5 |
|
value: 46.54 |
|
- type: precision_at_1 |
|
value: 40.127 |
|
- type: precision_at_10 |
|
value: 9.229 |
|
- type: precision_at_100 |
|
value: 1.473 |
|
- type: precision_at_1000 |
|
value: 0.19499999999999998 |
|
- type: precision_at_3 |
|
value: 21.444 |
|
- type: precision_at_5 |
|
value: 15.389 |
|
- type: recall_at_1 |
|
value: 32.126 |
|
- type: recall_at_10 |
|
value: 58.971 |
|
- type: recall_at_100 |
|
value: 76.115 |
|
- type: recall_at_1000 |
|
value: 89.556 |
|
- type: recall_at_3 |
|
value: 45.891 |
|
- type: recall_at_5 |
|
value: 52.242 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 41.312 |
|
- type: map_at_10 |
|
value: 54.510000000000005 |
|
- type: map_at_100 |
|
value: 55.544000000000004 |
|
- type: map_at_1000 |
|
value: 55.593 |
|
- type: map_at_3 |
|
value: 50.859 |
|
- type: map_at_5 |
|
value: 52.839999999999996 |
|
- type: mrr_at_1 |
|
value: 47.147 |
|
- type: mrr_at_10 |
|
value: 57.678 |
|
- type: mrr_at_100 |
|
value: 58.287 |
|
- type: mrr_at_1000 |
|
value: 58.312 |
|
- type: mrr_at_3 |
|
value: 55.025999999999996 |
|
- type: mrr_at_5 |
|
value: 56.55 |
|
- type: ndcg_at_1 |
|
value: 47.147 |
|
- type: ndcg_at_10 |
|
value: 60.672000000000004 |
|
- type: ndcg_at_100 |
|
value: 64.411 |
|
- type: ndcg_at_1000 |
|
value: 65.35499999999999 |
|
- type: ndcg_at_3 |
|
value: 54.643 |
|
- type: ndcg_at_5 |
|
value: 57.461 |
|
- type: precision_at_1 |
|
value: 47.147 |
|
- type: precision_at_10 |
|
value: 9.881 |
|
- type: precision_at_100 |
|
value: 1.27 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 24.556 |
|
- type: precision_at_5 |
|
value: 16.814999999999998 |
|
- type: recall_at_1 |
|
value: 41.312 |
|
- type: recall_at_10 |
|
value: 75.62299999999999 |
|
- type: recall_at_100 |
|
value: 91.388 |
|
- type: recall_at_1000 |
|
value: 98.08 |
|
- type: recall_at_3 |
|
value: 59.40299999999999 |
|
- type: recall_at_5 |
|
value: 66.43900000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.609 |
|
- type: map_at_10 |
|
value: 37.614 |
|
- type: map_at_100 |
|
value: 38.584 |
|
- type: map_at_1000 |
|
value: 38.652 |
|
- type: map_at_3 |
|
value: 34.731 |
|
- type: map_at_5 |
|
value: 36.308 |
|
- type: mrr_at_1 |
|
value: 29.944 |
|
- type: mrr_at_10 |
|
value: 39.829 |
|
- type: mrr_at_100 |
|
value: 40.659 |
|
- type: mrr_at_1000 |
|
value: 40.709 |
|
- type: mrr_at_3 |
|
value: 37.269000000000005 |
|
- type: mrr_at_5 |
|
value: 38.625 |
|
- type: ndcg_at_1 |
|
value: 29.944 |
|
- type: ndcg_at_10 |
|
value: 43.082 |
|
- type: ndcg_at_100 |
|
value: 47.857 |
|
- type: ndcg_at_1000 |
|
value: 49.612 |
|
- type: ndcg_at_3 |
|
value: 37.578 |
|
- type: ndcg_at_5 |
|
value: 40.135 |
|
- type: precision_at_1 |
|
value: 29.944 |
|
- type: precision_at_10 |
|
value: 6.678000000000001 |
|
- type: precision_at_100 |
|
value: 0.951 |
|
- type: precision_at_1000 |
|
value: 0.11399999999999999 |
|
- type: precision_at_3 |
|
value: 16.045 |
|
- type: precision_at_5 |
|
value: 11.073 |
|
- type: recall_at_1 |
|
value: 27.609 |
|
- type: recall_at_10 |
|
value: 57.718 |
|
- type: recall_at_100 |
|
value: 79.768 |
|
- type: recall_at_1000 |
|
value: 92.868 |
|
- type: recall_at_3 |
|
value: 42.876 |
|
- type: recall_at_5 |
|
value: 49.104 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.071 |
|
- type: map_at_10 |
|
value: 27.471 |
|
- type: map_at_100 |
|
value: 28.71 |
|
- type: map_at_1000 |
|
value: 28.833 |
|
- type: map_at_3 |
|
value: 24.698 |
|
- type: map_at_5 |
|
value: 26.461000000000002 |
|
- type: mrr_at_1 |
|
value: 22.387999999999998 |
|
- type: mrr_at_10 |
|
value: 32.522 |
|
- type: mrr_at_100 |
|
value: 33.393 |
|
- type: mrr_at_1000 |
|
value: 33.455 |
|
- type: mrr_at_3 |
|
value: 29.830000000000002 |
|
- type: mrr_at_5 |
|
value: 31.472 |
|
- type: ndcg_at_1 |
|
value: 22.387999999999998 |
|
- type: ndcg_at_10 |
|
value: 33.278999999999996 |
|
- type: ndcg_at_100 |
|
value: 39.043 |
|
- type: ndcg_at_1000 |
|
value: 41.763 |
|
- type: ndcg_at_3 |
|
value: 28.310999999999996 |
|
- type: ndcg_at_5 |
|
value: 31.007 |
|
- type: precision_at_1 |
|
value: 22.387999999999998 |
|
- type: precision_at_10 |
|
value: 6.157 |
|
- type: precision_at_100 |
|
value: 1.042 |
|
- type: precision_at_1000 |
|
value: 0.14200000000000002 |
|
- type: precision_at_3 |
|
value: 13.972000000000001 |
|
- type: precision_at_5 |
|
value: 10.274 |
|
- type: recall_at_1 |
|
value: 18.071 |
|
- type: recall_at_10 |
|
value: 46.025 |
|
- type: recall_at_100 |
|
value: 71.153 |
|
- type: recall_at_1000 |
|
value: 90.232 |
|
- type: recall_at_3 |
|
value: 32.311 |
|
- type: recall_at_5 |
|
value: 39.296 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.813000000000002 |
|
- type: map_at_10 |
|
value: 42.594 |
|
- type: map_at_100 |
|
value: 43.949 |
|
- type: map_at_1000 |
|
value: 44.052 |
|
- type: map_at_3 |
|
value: 39.1 |
|
- type: map_at_5 |
|
value: 41.111 |
|
- type: mrr_at_1 |
|
value: 37.824999999999996 |
|
- type: mrr_at_10 |
|
value: 48.06 |
|
- type: mrr_at_100 |
|
value: 48.91 |
|
- type: mrr_at_1000 |
|
value: 48.946 |
|
- type: mrr_at_3 |
|
value: 45.509 |
|
- type: mrr_at_5 |
|
value: 47.073 |
|
- type: ndcg_at_1 |
|
value: 37.824999999999996 |
|
- type: ndcg_at_10 |
|
value: 48.882 |
|
- type: ndcg_at_100 |
|
value: 54.330999999999996 |
|
- type: ndcg_at_1000 |
|
value: 56.120999999999995 |
|
- type: ndcg_at_3 |
|
value: 43.529 |
|
- type: ndcg_at_5 |
|
value: 46.217999999999996 |
|
- type: precision_at_1 |
|
value: 37.824999999999996 |
|
- type: precision_at_10 |
|
value: 8.845 |
|
- type: precision_at_100 |
|
value: 1.34 |
|
- type: precision_at_1000 |
|
value: 0.168 |
|
- type: precision_at_3 |
|
value: 20.757 |
|
- type: precision_at_5 |
|
value: 14.802999999999999 |
|
- type: recall_at_1 |
|
value: 30.813000000000002 |
|
- type: recall_at_10 |
|
value: 61.895999999999994 |
|
- type: recall_at_100 |
|
value: 84.513 |
|
- type: recall_at_1000 |
|
value: 95.817 |
|
- type: recall_at_3 |
|
value: 47.099000000000004 |
|
- type: recall_at_5 |
|
value: 54.031 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.735999999999997 |
|
- type: map_at_10 |
|
value: 36.799 |
|
- type: map_at_100 |
|
value: 38.246 |
|
- type: map_at_1000 |
|
value: 38.353 |
|
- type: map_at_3 |
|
value: 33.133 |
|
- type: map_at_5 |
|
value: 34.954 |
|
- type: mrr_at_1 |
|
value: 31.849 |
|
- type: mrr_at_10 |
|
value: 41.928 |
|
- type: mrr_at_100 |
|
value: 42.846000000000004 |
|
- type: mrr_at_1000 |
|
value: 42.894 |
|
- type: mrr_at_3 |
|
value: 39.117000000000004 |
|
- type: mrr_at_5 |
|
value: 40.521 |
|
- type: ndcg_at_1 |
|
value: 31.849 |
|
- type: ndcg_at_10 |
|
value: 43.143 |
|
- type: ndcg_at_100 |
|
value: 48.963 |
|
- type: ndcg_at_1000 |
|
value: 51.041000000000004 |
|
- type: ndcg_at_3 |
|
value: 37.218 |
|
- type: ndcg_at_5 |
|
value: 39.542 |
|
- type: precision_at_1 |
|
value: 31.849 |
|
- type: precision_at_10 |
|
value: 8.231 |
|
- type: precision_at_100 |
|
value: 1.277 |
|
- type: precision_at_1000 |
|
value: 0.164 |
|
- type: precision_at_3 |
|
value: 18.037 |
|
- type: precision_at_5 |
|
value: 12.945 |
|
- type: recall_at_1 |
|
value: 25.735999999999997 |
|
- type: recall_at_10 |
|
value: 56.735 |
|
- type: recall_at_100 |
|
value: 81.04 |
|
- type: recall_at_1000 |
|
value: 94.845 |
|
- type: recall_at_3 |
|
value: 40.239999999999995 |
|
- type: recall_at_5 |
|
value: 46.378 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.580333333333336 |
|
- type: map_at_10 |
|
value: 37.70558333333334 |
|
- type: map_at_100 |
|
value: 38.94941666666667 |
|
- type: map_at_1000 |
|
value: 39.062083333333334 |
|
- type: map_at_3 |
|
value: 34.63333333333334 |
|
- type: map_at_5 |
|
value: 36.35241666666666 |
|
- type: mrr_at_1 |
|
value: 32.64866666666667 |
|
- type: mrr_at_10 |
|
value: 42.018499999999996 |
|
- type: mrr_at_100 |
|
value: 42.83391666666666 |
|
- type: mrr_at_1000 |
|
value: 42.884166666666665 |
|
- type: mrr_at_3 |
|
value: 39.476499999999994 |
|
- type: mrr_at_5 |
|
value: 40.96983333333334 |
|
- type: ndcg_at_1 |
|
value: 32.64866666666667 |
|
- type: ndcg_at_10 |
|
value: 43.43866666666667 |
|
- type: ndcg_at_100 |
|
value: 48.569833333333335 |
|
- type: ndcg_at_1000 |
|
value: 50.6495 |
|
- type: ndcg_at_3 |
|
value: 38.327166666666656 |
|
- type: ndcg_at_5 |
|
value: 40.76941666666667 |
|
- type: precision_at_1 |
|
value: 32.64866666666667 |
|
- type: precision_at_10 |
|
value: 7.652333333333332 |
|
- type: precision_at_100 |
|
value: 1.2066666666666666 |
|
- type: precision_at_1000 |
|
value: 0.15841666666666668 |
|
- type: precision_at_3 |
|
value: 17.75108333333333 |
|
- type: precision_at_5 |
|
value: 12.641916666666669 |
|
- type: recall_at_1 |
|
value: 27.580333333333336 |
|
- type: recall_at_10 |
|
value: 56.02591666666667 |
|
- type: recall_at_100 |
|
value: 78.317 |
|
- type: recall_at_1000 |
|
value: 92.52608333333332 |
|
- type: recall_at_3 |
|
value: 41.84283333333333 |
|
- type: recall_at_5 |
|
value: 48.105666666666664 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.876 |
|
- type: map_at_10 |
|
value: 34.521 |
|
- type: map_at_100 |
|
value: 35.581 |
|
- type: map_at_1000 |
|
value: 35.674 |
|
- type: map_at_3 |
|
value: 32.501000000000005 |
|
- type: map_at_5 |
|
value: 33.602 |
|
- type: mrr_at_1 |
|
value: 31.441999999999997 |
|
- type: mrr_at_10 |
|
value: 37.669999999999995 |
|
- type: mrr_at_100 |
|
value: 38.523 |
|
- type: mrr_at_1000 |
|
value: 38.59 |
|
- type: mrr_at_3 |
|
value: 35.762 |
|
- type: mrr_at_5 |
|
value: 36.812 |
|
- type: ndcg_at_1 |
|
value: 31.441999999999997 |
|
- type: ndcg_at_10 |
|
value: 38.46 |
|
- type: ndcg_at_100 |
|
value: 43.479 |
|
- type: ndcg_at_1000 |
|
value: 45.858 |
|
- type: ndcg_at_3 |
|
value: 34.668 |
|
- type: ndcg_at_5 |
|
value: 36.416 |
|
- type: precision_at_1 |
|
value: 31.441999999999997 |
|
- type: precision_at_10 |
|
value: 5.782 |
|
- type: precision_at_100 |
|
value: 0.91 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 14.417 |
|
- type: precision_at_5 |
|
value: 9.876999999999999 |
|
- type: recall_at_1 |
|
value: 27.876 |
|
- type: recall_at_10 |
|
value: 47.556 |
|
- type: recall_at_100 |
|
value: 70.39699999999999 |
|
- type: recall_at_1000 |
|
value: 87.969 |
|
- type: recall_at_3 |
|
value: 37.226 |
|
- type: recall_at_5 |
|
value: 41.43 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.854000000000003 |
|
- type: map_at_10 |
|
value: 26.632 |
|
- type: map_at_100 |
|
value: 27.849 |
|
- type: map_at_1000 |
|
value: 27.977 |
|
- type: map_at_3 |
|
value: 24.089 |
|
- type: map_at_5 |
|
value: 25.477 |
|
- type: mrr_at_1 |
|
value: 22.987 |
|
- type: mrr_at_10 |
|
value: 30.781999999999996 |
|
- type: mrr_at_100 |
|
value: 31.746000000000002 |
|
- type: mrr_at_1000 |
|
value: 31.818 |
|
- type: mrr_at_3 |
|
value: 28.43 |
|
- type: mrr_at_5 |
|
value: 29.791 |
|
- type: ndcg_at_1 |
|
value: 22.987 |
|
- type: ndcg_at_10 |
|
value: 31.585 |
|
- type: ndcg_at_100 |
|
value: 37.32 |
|
- type: ndcg_at_1000 |
|
value: 40.072 |
|
- type: ndcg_at_3 |
|
value: 27.058 |
|
- type: ndcg_at_5 |
|
value: 29.137999999999998 |
|
- type: precision_at_1 |
|
value: 22.987 |
|
- type: precision_at_10 |
|
value: 5.76 |
|
- type: precision_at_100 |
|
value: 1.018 |
|
- type: precision_at_1000 |
|
value: 0.14400000000000002 |
|
- type: precision_at_3 |
|
value: 12.767000000000001 |
|
- type: precision_at_5 |
|
value: 9.257 |
|
- type: recall_at_1 |
|
value: 18.854000000000003 |
|
- type: recall_at_10 |
|
value: 42.349 |
|
- type: recall_at_100 |
|
value: 68.15299999999999 |
|
- type: recall_at_1000 |
|
value: 87.44 |
|
- type: recall_at_3 |
|
value: 29.715999999999998 |
|
- type: recall_at_5 |
|
value: 35.085 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.094 |
|
- type: map_at_10 |
|
value: 38.22 |
|
- type: map_at_100 |
|
value: 39.352 |
|
- type: map_at_1000 |
|
value: 39.452 |
|
- type: map_at_3 |
|
value: 35.339 |
|
- type: map_at_5 |
|
value: 36.78 |
|
- type: mrr_at_1 |
|
value: 33.022 |
|
- type: mrr_at_10 |
|
value: 42.466 |
|
- type: mrr_at_100 |
|
value: 43.3 |
|
- type: mrr_at_1000 |
|
value: 43.356 |
|
- type: mrr_at_3 |
|
value: 40.159 |
|
- type: mrr_at_5 |
|
value: 41.272999999999996 |
|
- type: ndcg_at_1 |
|
value: 33.022 |
|
- type: ndcg_at_10 |
|
value: 43.976 |
|
- type: ndcg_at_100 |
|
value: 49.008 |
|
- type: ndcg_at_1000 |
|
value: 51.154999999999994 |
|
- type: ndcg_at_3 |
|
value: 38.891 |
|
- type: ndcg_at_5 |
|
value: 40.897 |
|
- type: precision_at_1 |
|
value: 33.022 |
|
- type: precision_at_10 |
|
value: 7.396999999999999 |
|
- type: precision_at_100 |
|
value: 1.1199999999999999 |
|
- type: precision_at_1000 |
|
value: 0.14200000000000002 |
|
- type: precision_at_3 |
|
value: 17.724 |
|
- type: precision_at_5 |
|
value: 12.239 |
|
- type: recall_at_1 |
|
value: 28.094 |
|
- type: recall_at_10 |
|
value: 57.162 |
|
- type: recall_at_100 |
|
value: 78.636 |
|
- type: recall_at_1000 |
|
value: 93.376 |
|
- type: recall_at_3 |
|
value: 43.328 |
|
- type: recall_at_5 |
|
value: 48.252 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.937 |
|
- type: map_at_10 |
|
value: 34.82 |
|
- type: map_at_100 |
|
value: 36.405 |
|
- type: map_at_1000 |
|
value: 36.626 |
|
- type: map_at_3 |
|
value: 31.548 |
|
- type: map_at_5 |
|
value: 33.355000000000004 |
|
- type: mrr_at_1 |
|
value: 30.435000000000002 |
|
- type: mrr_at_10 |
|
value: 39.946 |
|
- type: mrr_at_100 |
|
value: 40.873 |
|
- type: mrr_at_1000 |
|
value: 40.910000000000004 |
|
- type: mrr_at_3 |
|
value: 37.088 |
|
- type: mrr_at_5 |
|
value: 38.808 |
|
- type: ndcg_at_1 |
|
value: 30.435000000000002 |
|
- type: ndcg_at_10 |
|
value: 41.25 |
|
- type: ndcg_at_100 |
|
value: 47.229 |
|
- type: ndcg_at_1000 |
|
value: 49.395 |
|
- type: ndcg_at_3 |
|
value: 35.801 |
|
- type: ndcg_at_5 |
|
value: 38.457 |
|
- type: precision_at_1 |
|
value: 30.435000000000002 |
|
- type: precision_at_10 |
|
value: 8.083 |
|
- type: precision_at_100 |
|
value: 1.601 |
|
- type: precision_at_1000 |
|
value: 0.247 |
|
- type: precision_at_3 |
|
value: 17.061999999999998 |
|
- type: precision_at_5 |
|
value: 12.767000000000001 |
|
- type: recall_at_1 |
|
value: 24.937 |
|
- type: recall_at_10 |
|
value: 53.905 |
|
- type: recall_at_100 |
|
value: 80.607 |
|
- type: recall_at_1000 |
|
value: 93.728 |
|
- type: recall_at_3 |
|
value: 38.446000000000005 |
|
- type: recall_at_5 |
|
value: 45.188 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.095000000000002 |
|
- type: map_at_10 |
|
value: 30.935000000000002 |
|
- type: map_at_100 |
|
value: 31.907000000000004 |
|
- type: map_at_1000 |
|
value: 32.006 |
|
- type: map_at_3 |
|
value: 28.242 |
|
- type: map_at_5 |
|
value: 29.963 |
|
- type: mrr_at_1 |
|
value: 23.845 |
|
- type: mrr_at_10 |
|
value: 32.978 |
|
- type: mrr_at_100 |
|
value: 33.802 |
|
- type: mrr_at_1000 |
|
value: 33.867000000000004 |
|
- type: mrr_at_3 |
|
value: 30.314000000000004 |
|
- type: mrr_at_5 |
|
value: 32.089 |
|
- type: ndcg_at_1 |
|
value: 23.845 |
|
- type: ndcg_at_10 |
|
value: 35.934 |
|
- type: ndcg_at_100 |
|
value: 40.598 |
|
- type: ndcg_at_1000 |
|
value: 43.089 |
|
- type: ndcg_at_3 |
|
value: 30.776999999999997 |
|
- type: ndcg_at_5 |
|
value: 33.711999999999996 |
|
- type: precision_at_1 |
|
value: 23.845 |
|
- type: precision_at_10 |
|
value: 5.656 |
|
- type: precision_at_100 |
|
value: 0.861 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 13.247 |
|
- type: precision_at_5 |
|
value: 9.612 |
|
- type: recall_at_1 |
|
value: 22.095000000000002 |
|
- type: recall_at_10 |
|
value: 49.25 |
|
- type: recall_at_100 |
|
value: 70.482 |
|
- type: recall_at_1000 |
|
value: 88.98899999999999 |
|
- type: recall_at_3 |
|
value: 35.619 |
|
- type: recall_at_5 |
|
value: 42.674 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.154 |
|
- type: map_at_10 |
|
value: 24.654999999999998 |
|
- type: map_at_100 |
|
value: 26.723999999999997 |
|
- type: map_at_1000 |
|
value: 26.912000000000003 |
|
- type: map_at_3 |
|
value: 20.4 |
|
- type: map_at_5 |
|
value: 22.477 |
|
- type: mrr_at_1 |
|
value: 32.117000000000004 |
|
- type: mrr_at_10 |
|
value: 44.590999999999994 |
|
- type: mrr_at_100 |
|
value: 45.425 |
|
- type: mrr_at_1000 |
|
value: 45.456 |
|
- type: mrr_at_3 |
|
value: 41.281 |
|
- type: mrr_at_5 |
|
value: 43.219 |
|
- type: ndcg_at_1 |
|
value: 32.117000000000004 |
|
- type: ndcg_at_10 |
|
value: 33.994 |
|
- type: ndcg_at_100 |
|
value: 41.438 |
|
- type: ndcg_at_1000 |
|
value: 44.611000000000004 |
|
- type: ndcg_at_3 |
|
value: 27.816000000000003 |
|
- type: ndcg_at_5 |
|
value: 29.816 |
|
- type: precision_at_1 |
|
value: 32.117000000000004 |
|
- type: precision_at_10 |
|
value: 10.756 |
|
- type: precision_at_100 |
|
value: 1.8679999999999999 |
|
- type: precision_at_1000 |
|
value: 0.246 |
|
- type: precision_at_3 |
|
value: 20.803 |
|
- type: precision_at_5 |
|
value: 15.987000000000002 |
|
- type: recall_at_1 |
|
value: 14.154 |
|
- type: recall_at_10 |
|
value: 40.489999999999995 |
|
- type: recall_at_100 |
|
value: 65.635 |
|
- type: recall_at_1000 |
|
value: 83.276 |
|
- type: recall_at_3 |
|
value: 25.241000000000003 |
|
- type: recall_at_5 |
|
value: 31.211 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.332 |
|
- type: map_at_10 |
|
value: 20.462 |
|
- type: map_at_100 |
|
value: 29.473 |
|
- type: map_at_1000 |
|
value: 31.215 |
|
- type: map_at_3 |
|
value: 14.466999999999999 |
|
- type: map_at_5 |
|
value: 16.922 |
|
- type: mrr_at_1 |
|
value: 69.5 |
|
- type: mrr_at_10 |
|
value: 77.039 |
|
- type: mrr_at_100 |
|
value: 77.265 |
|
- type: mrr_at_1000 |
|
value: 77.271 |
|
- type: mrr_at_3 |
|
value: 75.5 |
|
- type: mrr_at_5 |
|
value: 76.4 |
|
- type: ndcg_at_1 |
|
value: 57.125 |
|
- type: ndcg_at_10 |
|
value: 42.958 |
|
- type: ndcg_at_100 |
|
value: 48.396 |
|
- type: ndcg_at_1000 |
|
value: 55.897 |
|
- type: ndcg_at_3 |
|
value: 47.188 |
|
- type: ndcg_at_5 |
|
value: 44.376 |
|
- type: precision_at_1 |
|
value: 69.5 |
|
- type: precision_at_10 |
|
value: 34.5 |
|
- type: precision_at_100 |
|
value: 11.18 |
|
- type: precision_at_1000 |
|
value: 2.13 |
|
- type: precision_at_3 |
|
value: 51.083 |
|
- type: precision_at_5 |
|
value: 43.1 |
|
- type: recall_at_1 |
|
value: 9.332 |
|
- type: recall_at_10 |
|
value: 26.422 |
|
- type: recall_at_100 |
|
value: 56.098000000000006 |
|
- type: recall_at_1000 |
|
value: 79.66 |
|
- type: recall_at_3 |
|
value: 15.703 |
|
- type: recall_at_5 |
|
value: 19.644000000000002 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 54.72 |
|
- type: f1 |
|
value: 49.67819606587526 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 74.97 |
|
- type: map_at_10 |
|
value: 82.956 |
|
- type: map_at_100 |
|
value: 83.193 |
|
- type: map_at_1000 |
|
value: 83.208 |
|
- type: map_at_3 |
|
value: 81.837 |
|
- type: map_at_5 |
|
value: 82.57 |
|
- type: mrr_at_1 |
|
value: 80.783 |
|
- type: mrr_at_10 |
|
value: 87.546 |
|
- type: mrr_at_100 |
|
value: 87.627 |
|
- type: mrr_at_1000 |
|
value: 87.63 |
|
- type: mrr_at_3 |
|
value: 86.79400000000001 |
|
- type: mrr_at_5 |
|
value: 87.32799999999999 |
|
- type: ndcg_at_1 |
|
value: 80.783 |
|
- type: ndcg_at_10 |
|
value: 86.54899999999999 |
|
- type: ndcg_at_100 |
|
value: 87.355 |
|
- type: ndcg_at_1000 |
|
value: 87.629 |
|
- type: ndcg_at_3 |
|
value: 84.82 |
|
- type: ndcg_at_5 |
|
value: 85.83800000000001 |
|
- type: precision_at_1 |
|
value: 80.783 |
|
- type: precision_at_10 |
|
value: 10.327 |
|
- type: precision_at_100 |
|
value: 1.094 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 32.218 |
|
- type: precision_at_5 |
|
value: 20.012 |
|
- type: recall_at_1 |
|
value: 74.97 |
|
- type: recall_at_10 |
|
value: 93.072 |
|
- type: recall_at_100 |
|
value: 96.218 |
|
- type: recall_at_1000 |
|
value: 97.991 |
|
- type: recall_at_3 |
|
value: 88.357 |
|
- type: recall_at_5 |
|
value: 90.983 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.12 |
|
- type: map_at_10 |
|
value: 35.908 |
|
- type: map_at_100 |
|
value: 37.895 |
|
- type: map_at_1000 |
|
value: 38.068000000000005 |
|
- type: map_at_3 |
|
value: 31.189 |
|
- type: map_at_5 |
|
value: 33.908 |
|
- type: mrr_at_1 |
|
value: 42.901 |
|
- type: mrr_at_10 |
|
value: 52.578 |
|
- type: mrr_at_100 |
|
value: 53.308 |
|
- type: mrr_at_1000 |
|
value: 53.342 |
|
- type: mrr_at_3 |
|
value: 50.385999999999996 |
|
- type: mrr_at_5 |
|
value: 51.62799999999999 |
|
- type: ndcg_at_1 |
|
value: 42.901 |
|
- type: ndcg_at_10 |
|
value: 44.302 |
|
- type: ndcg_at_100 |
|
value: 51.132999999999996 |
|
- type: ndcg_at_1000 |
|
value: 53.848 |
|
- type: ndcg_at_3 |
|
value: 40.464 |
|
- type: ndcg_at_5 |
|
value: 41.743 |
|
- type: precision_at_1 |
|
value: 42.901 |
|
- type: precision_at_10 |
|
value: 12.423 |
|
- type: precision_at_100 |
|
value: 1.968 |
|
- type: precision_at_1000 |
|
value: 0.246 |
|
- type: precision_at_3 |
|
value: 27.622999999999998 |
|
- type: precision_at_5 |
|
value: 20.278 |
|
- type: recall_at_1 |
|
value: 21.12 |
|
- type: recall_at_10 |
|
value: 52.091 |
|
- type: recall_at_100 |
|
value: 77.062 |
|
- type: recall_at_1000 |
|
value: 93.082 |
|
- type: recall_at_3 |
|
value: 37.223 |
|
- type: recall_at_5 |
|
value: 43.826 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.940000000000005 |
|
- type: map_at_10 |
|
value: 62.239999999999995 |
|
- type: map_at_100 |
|
value: 63.141000000000005 |
|
- type: map_at_1000 |
|
value: 63.205999999999996 |
|
- type: map_at_3 |
|
value: 58.738 |
|
- type: map_at_5 |
|
value: 60.924 |
|
- type: mrr_at_1 |
|
value: 77.88000000000001 |
|
- type: mrr_at_10 |
|
value: 83.7 |
|
- type: mrr_at_100 |
|
value: 83.882 |
|
- type: mrr_at_1000 |
|
value: 83.889 |
|
- type: mrr_at_3 |
|
value: 82.748 |
|
- type: mrr_at_5 |
|
value: 83.381 |
|
- type: ndcg_at_1 |
|
value: 77.88000000000001 |
|
- type: ndcg_at_10 |
|
value: 70.462 |
|
- type: ndcg_at_100 |
|
value: 73.564 |
|
- type: ndcg_at_1000 |
|
value: 74.78099999999999 |
|
- type: ndcg_at_3 |
|
value: 65.524 |
|
- type: ndcg_at_5 |
|
value: 68.282 |
|
- type: precision_at_1 |
|
value: 77.88000000000001 |
|
- type: precision_at_10 |
|
value: 14.81 |
|
- type: precision_at_100 |
|
value: 1.7229999999999999 |
|
- type: precision_at_1000 |
|
value: 0.188 |
|
- type: precision_at_3 |
|
value: 42.083999999999996 |
|
- type: precision_at_5 |
|
value: 27.43 |
|
- type: recall_at_1 |
|
value: 38.940000000000005 |
|
- type: recall_at_10 |
|
value: 74.051 |
|
- type: recall_at_100 |
|
value: 86.158 |
|
- type: recall_at_1000 |
|
value: 94.146 |
|
- type: recall_at_3 |
|
value: 63.126000000000005 |
|
- type: recall_at_5 |
|
value: 68.575 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 91.23440000000001 |
|
- type: ap |
|
value: 87.33490392265892 |
|
- type: f1 |
|
value: 91.21374626021836 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.137999999999998 |
|
- type: map_at_10 |
|
value: 34.471000000000004 |
|
- type: map_at_100 |
|
value: 35.634 |
|
- type: map_at_1000 |
|
value: 35.685 |
|
- type: map_at_3 |
|
value: 30.587999999999997 |
|
- type: map_at_5 |
|
value: 32.812999999999995 |
|
- type: mrr_at_1 |
|
value: 22.736 |
|
- type: mrr_at_10 |
|
value: 35.092 |
|
- type: mrr_at_100 |
|
value: 36.193999999999996 |
|
- type: mrr_at_1000 |
|
value: 36.238 |
|
- type: mrr_at_3 |
|
value: 31.28 |
|
- type: mrr_at_5 |
|
value: 33.498 |
|
- type: ndcg_at_1 |
|
value: 22.736 |
|
- type: ndcg_at_10 |
|
value: 41.388999999999996 |
|
- type: ndcg_at_100 |
|
value: 46.967999999999996 |
|
- type: ndcg_at_1000 |
|
value: 48.178 |
|
- type: ndcg_at_3 |
|
value: 33.503 |
|
- type: ndcg_at_5 |
|
value: 37.484 |
|
- type: precision_at_1 |
|
value: 22.736 |
|
- type: precision_at_10 |
|
value: 6.54 |
|
- type: precision_at_100 |
|
value: 0.9339999999999999 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.249999999999998 |
|
- type: precision_at_5 |
|
value: 10.562000000000001 |
|
- type: recall_at_1 |
|
value: 22.137999999999998 |
|
- type: recall_at_10 |
|
value: 62.629999999999995 |
|
- type: recall_at_100 |
|
value: 88.375 |
|
- type: recall_at_1000 |
|
value: 97.529 |
|
- type: recall_at_3 |
|
value: 41.245 |
|
- type: recall_at_5 |
|
value: 50.808 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 95.25079799361606 |
|
- type: f1 |
|
value: 95.00726023695032 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 78.23757409940721 |
|
- type: f1 |
|
value: 58.534958803195714 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 76.20040349697378 |
|
- type: f1 |
|
value: 74.31261149784696 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 79.35104236718227 |
|
- type: f1 |
|
value: 79.7373049864316 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 34.478828180753126 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 32.25696147904426 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 32.82488548405117 |
|
- type: mrr |
|
value: 34.066706809031096 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.557 |
|
- type: map_at_10 |
|
value: 15.055 |
|
- type: map_at_100 |
|
value: 19.575 |
|
- type: map_at_1000 |
|
value: 21.267 |
|
- type: map_at_3 |
|
value: 10.86 |
|
- type: map_at_5 |
|
value: 12.83 |
|
- type: mrr_at_1 |
|
value: 50.464 |
|
- type: mrr_at_10 |
|
value: 59.050999999999995 |
|
- type: mrr_at_100 |
|
value: 59.436 |
|
- type: mrr_at_1000 |
|
value: 59.476 |
|
- type: mrr_at_3 |
|
value: 56.811 |
|
- type: mrr_at_5 |
|
value: 58.08 |
|
- type: ndcg_at_1 |
|
value: 47.988 |
|
- type: ndcg_at_10 |
|
value: 38.645 |
|
- type: ndcg_at_100 |
|
value: 36.339 |
|
- type: ndcg_at_1000 |
|
value: 45.279 |
|
- type: ndcg_at_3 |
|
value: 43.35 |
|
- type: ndcg_at_5 |
|
value: 41.564 |
|
- type: precision_at_1 |
|
value: 49.845 |
|
- type: precision_at_10 |
|
value: 28.544999999999998 |
|
- type: precision_at_100 |
|
value: 9.322 |
|
- type: precision_at_1000 |
|
value: 2.258 |
|
- type: precision_at_3 |
|
value: 40.144000000000005 |
|
- type: precision_at_5 |
|
value: 35.913000000000004 |
|
- type: recall_at_1 |
|
value: 6.557 |
|
- type: recall_at_10 |
|
value: 19.5 |
|
- type: recall_at_100 |
|
value: 37.153999999999996 |
|
- type: recall_at_1000 |
|
value: 69.581 |
|
- type: recall_at_3 |
|
value: 12.133 |
|
- type: recall_at_5 |
|
value: 15.43 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 31.740000000000002 |
|
- type: map_at_10 |
|
value: 48.150999999999996 |
|
- type: map_at_100 |
|
value: 49.125 |
|
- type: map_at_1000 |
|
value: 49.149 |
|
- type: map_at_3 |
|
value: 43.645 |
|
- type: map_at_5 |
|
value: 46.417 |
|
- type: mrr_at_1 |
|
value: 35.892 |
|
- type: mrr_at_10 |
|
value: 50.524 |
|
- type: mrr_at_100 |
|
value: 51.232 |
|
- type: mrr_at_1000 |
|
value: 51.24999999999999 |
|
- type: mrr_at_3 |
|
value: 46.852 |
|
- type: mrr_at_5 |
|
value: 49.146 |
|
- type: ndcg_at_1 |
|
value: 35.892 |
|
- type: ndcg_at_10 |
|
value: 56.08800000000001 |
|
- type: ndcg_at_100 |
|
value: 60.077000000000005 |
|
- type: ndcg_at_1000 |
|
value: 60.632 |
|
- type: ndcg_at_3 |
|
value: 47.765 |
|
- type: ndcg_at_5 |
|
value: 52.322 |
|
- type: precision_at_1 |
|
value: 35.892 |
|
- type: precision_at_10 |
|
value: 9.296 |
|
- type: precision_at_100 |
|
value: 1.154 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 21.92 |
|
- type: precision_at_5 |
|
value: 15.781999999999998 |
|
- type: recall_at_1 |
|
value: 31.740000000000002 |
|
- type: recall_at_10 |
|
value: 77.725 |
|
- type: recall_at_100 |
|
value: 94.841 |
|
- type: recall_at_1000 |
|
value: 99.003 |
|
- type: recall_at_3 |
|
value: 56.407 |
|
- type: recall_at_5 |
|
value: 66.848 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.429 |
|
- type: map_at_10 |
|
value: 85.42699999999999 |
|
- type: map_at_100 |
|
value: 86.063 |
|
- type: map_at_1000 |
|
value: 86.077 |
|
- type: map_at_3 |
|
value: 82.573 |
|
- type: map_at_5 |
|
value: 84.371 |
|
- type: mrr_at_1 |
|
value: 82.34 |
|
- type: mrr_at_10 |
|
value: 88.247 |
|
- type: mrr_at_100 |
|
value: 88.357 |
|
- type: mrr_at_1000 |
|
value: 88.357 |
|
- type: mrr_at_3 |
|
value: 87.38 |
|
- type: mrr_at_5 |
|
value: 87.981 |
|
- type: ndcg_at_1 |
|
value: 82.34 |
|
- type: ndcg_at_10 |
|
value: 88.979 |
|
- type: ndcg_at_100 |
|
value: 90.18599999999999 |
|
- type: ndcg_at_1000 |
|
value: 90.254 |
|
- type: ndcg_at_3 |
|
value: 86.378 |
|
- type: ndcg_at_5 |
|
value: 87.821 |
|
- type: precision_at_1 |
|
value: 82.34 |
|
- type: precision_at_10 |
|
value: 13.482 |
|
- type: precision_at_100 |
|
value: 1.537 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.852999999999994 |
|
- type: precision_at_5 |
|
value: 24.798000000000002 |
|
- type: recall_at_1 |
|
value: 71.429 |
|
- type: recall_at_10 |
|
value: 95.64099999999999 |
|
- type: recall_at_100 |
|
value: 99.723 |
|
- type: recall_at_1000 |
|
value: 99.98 |
|
- type: recall_at_3 |
|
value: 88.011 |
|
- type: recall_at_5 |
|
value: 92.246 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 60.62148584103299 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 63.2923987272903 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.128 |
|
- type: map_at_10 |
|
value: 14.63 |
|
- type: map_at_100 |
|
value: 17.285 |
|
- type: map_at_1000 |
|
value: 17.676 |
|
- type: map_at_3 |
|
value: 9.993 |
|
- type: map_at_5 |
|
value: 12.286999999999999 |
|
- type: mrr_at_1 |
|
value: 25.4 |
|
- type: mrr_at_10 |
|
value: 38.423 |
|
- type: mrr_at_100 |
|
value: 39.497 |
|
- type: mrr_at_1000 |
|
value: 39.531 |
|
- type: mrr_at_3 |
|
value: 34.9 |
|
- type: mrr_at_5 |
|
value: 37.01 |
|
- type: ndcg_at_1 |
|
value: 25.4 |
|
- type: ndcg_at_10 |
|
value: 24.062 |
|
- type: ndcg_at_100 |
|
value: 33.823 |
|
- type: ndcg_at_1000 |
|
value: 39.663 |
|
- type: ndcg_at_3 |
|
value: 22.246 |
|
- type: ndcg_at_5 |
|
value: 19.761 |
|
- type: precision_at_1 |
|
value: 25.4 |
|
- type: precision_at_10 |
|
value: 12.85 |
|
- type: precision_at_100 |
|
value: 2.71 |
|
- type: precision_at_1000 |
|
value: 0.41000000000000003 |
|
- type: precision_at_3 |
|
value: 21.4 |
|
- type: precision_at_5 |
|
value: 17.86 |
|
- type: recall_at_1 |
|
value: 5.128 |
|
- type: recall_at_10 |
|
value: 26.06 |
|
- type: recall_at_100 |
|
value: 54.993 |
|
- type: recall_at_1000 |
|
value: 83.165 |
|
- type: recall_at_3 |
|
value: 13.003 |
|
- type: recall_at_5 |
|
value: 18.117 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.5466779326323 |
|
- type: cos_sim_spearman |
|
value: 82.79782085421951 |
|
- type: euclidean_pearson |
|
value: 84.76929982677339 |
|
- type: euclidean_spearman |
|
value: 82.51802536005597 |
|
- type: manhattan_pearson |
|
value: 84.76736312526177 |
|
- type: manhattan_spearman |
|
value: 82.50799656335593 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.40486308108694 |
|
- type: cos_sim_spearman |
|
value: 77.12670500926937 |
|
- type: euclidean_pearson |
|
value: 85.23836845503847 |
|
- type: euclidean_spearman |
|
value: 78.41475117006176 |
|
- type: manhattan_pearson |
|
value: 85.24302039610805 |
|
- type: manhattan_spearman |
|
value: 78.4053162562707 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.83570289087565 |
|
- type: cos_sim_spearman |
|
value: 89.28563503553643 |
|
- type: euclidean_pearson |
|
value: 87.77516003996445 |
|
- type: euclidean_spearman |
|
value: 88.8656149534085 |
|
- type: manhattan_pearson |
|
value: 87.75568872417946 |
|
- type: manhattan_spearman |
|
value: 88.80445489340585 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.776406555485 |
|
- type: cos_sim_spearman |
|
value: 83.8288465070091 |
|
- type: euclidean_pearson |
|
value: 85.37827999808123 |
|
- type: euclidean_spearman |
|
value: 84.11079529992739 |
|
- type: manhattan_pearson |
|
value: 85.35336495689121 |
|
- type: manhattan_spearman |
|
value: 84.08618492649347 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.57644404820684 |
|
- type: cos_sim_spearman |
|
value: 89.69728364350713 |
|
- type: euclidean_pearson |
|
value: 88.28202320389443 |
|
- type: euclidean_spearman |
|
value: 88.9560567319321 |
|
- type: manhattan_pearson |
|
value: 88.29461100044172 |
|
- type: manhattan_spearman |
|
value: 88.96030920678558 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.05211938460621 |
|
- type: cos_sim_spearman |
|
value: 86.43413865667489 |
|
- type: euclidean_pearson |
|
value: 85.62760689259562 |
|
- type: euclidean_spearman |
|
value: 86.28867831982394 |
|
- type: manhattan_pearson |
|
value: 85.60828879163458 |
|
- type: manhattan_spearman |
|
value: 86.27823731462473 |
|
- 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: 90.00254140466377 |
|
- type: cos_sim_spearman |
|
value: 89.66118745178284 |
|
- type: euclidean_pearson |
|
value: 89.46985446236553 |
|
- type: euclidean_spearman |
|
value: 88.92649032371526 |
|
- type: manhattan_pearson |
|
value: 89.49600028180247 |
|
- type: manhattan_spearman |
|
value: 88.86948431519099 |
|
- 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: 68.93578321067938 |
|
- type: cos_sim_spearman |
|
value: 69.60639595839257 |
|
- type: euclidean_pearson |
|
value: 70.33485090574897 |
|
- type: euclidean_spearman |
|
value: 69.03380379185452 |
|
- type: manhattan_pearson |
|
value: 70.42097254943839 |
|
- type: manhattan_spearman |
|
value: 69.25296348304255 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.29588700755069 |
|
- type: cos_sim_spearman |
|
value: 88.30389489193672 |
|
- type: euclidean_pearson |
|
value: 87.60349838180346 |
|
- type: euclidean_spearman |
|
value: 87.91041868311692 |
|
- type: manhattan_pearson |
|
value: 87.59373630607907 |
|
- type: manhattan_spearman |
|
value: 87.88690174001724 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 87.8030655700857 |
|
- type: mrr |
|
value: 96.3950637234951 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 60.028000000000006 |
|
- type: map_at_10 |
|
value: 69.855 |
|
- type: map_at_100 |
|
value: 70.257 |
|
- type: map_at_1000 |
|
value: 70.283 |
|
- type: map_at_3 |
|
value: 66.769 |
|
- type: map_at_5 |
|
value: 68.679 |
|
- type: mrr_at_1 |
|
value: 62.666999999999994 |
|
- type: mrr_at_10 |
|
value: 70.717 |
|
- type: mrr_at_100 |
|
value: 71.00800000000001 |
|
- type: mrr_at_1000 |
|
value: 71.033 |
|
- type: mrr_at_3 |
|
value: 68.389 |
|
- type: mrr_at_5 |
|
value: 69.939 |
|
- type: ndcg_at_1 |
|
value: 62.666999999999994 |
|
- type: ndcg_at_10 |
|
value: 74.715 |
|
- type: ndcg_at_100 |
|
value: 76.364 |
|
- type: ndcg_at_1000 |
|
value: 76.89399999999999 |
|
- type: ndcg_at_3 |
|
value: 69.383 |
|
- type: ndcg_at_5 |
|
value: 72.322 |
|
- type: precision_at_1 |
|
value: 62.666999999999994 |
|
- type: precision_at_10 |
|
value: 10.067 |
|
- type: precision_at_100 |
|
value: 1.09 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 27.111 |
|
- type: precision_at_5 |
|
value: 18.267 |
|
- type: recall_at_1 |
|
value: 60.028000000000006 |
|
- type: recall_at_10 |
|
value: 88.822 |
|
- type: recall_at_100 |
|
value: 96.167 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 74.367 |
|
- type: recall_at_5 |
|
value: 81.661 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.84554455445544 |
|
- type: cos_sim_ap |
|
value: 96.54482863244152 |
|
- type: cos_sim_f1 |
|
value: 92.13709677419355 |
|
- type: cos_sim_precision |
|
value: 92.88617886178862 |
|
- type: cos_sim_recall |
|
value: 91.4 |
|
- type: dot_accuracy |
|
value: 99.76039603960396 |
|
- type: dot_ap |
|
value: 93.20115278887057 |
|
- type: dot_f1 |
|
value: 87.92079207920793 |
|
- type: dot_precision |
|
value: 87.05882352941177 |
|
- type: dot_recall |
|
value: 88.8 |
|
- type: euclidean_accuracy |
|
value: 99.84950495049505 |
|
- type: euclidean_ap |
|
value: 96.53268343961348 |
|
- type: euclidean_f1 |
|
value: 92.23697650663942 |
|
- type: euclidean_precision |
|
value: 94.258872651357 |
|
- type: euclidean_recall |
|
value: 90.3 |
|
- type: manhattan_accuracy |
|
value: 99.85346534653465 |
|
- type: manhattan_ap |
|
value: 96.54495433438355 |
|
- type: manhattan_f1 |
|
value: 92.51012145748987 |
|
- type: manhattan_precision |
|
value: 93.64754098360656 |
|
- type: manhattan_recall |
|
value: 91.4 |
|
- type: max_accuracy |
|
value: 99.85346534653465 |
|
- type: max_ap |
|
value: 96.54495433438355 |
|
- type: max_f1 |
|
value: 92.51012145748987 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 66.46940443952006 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 36.396194493841584 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 54.881717673695555 |
|
- type: mrr |
|
value: 55.73439224174519 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 31.438177268254087 |
|
- type: cos_sim_spearman |
|
value: 30.96177698848688 |
|
- type: dot_pearson |
|
value: 30.513850376431435 |
|
- type: dot_spearman |
|
value: 29.932421046509706 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.21 |
|
- type: map_at_10 |
|
value: 1.727 |
|
- type: map_at_100 |
|
value: 9.881 |
|
- type: map_at_1000 |
|
value: 24.245 |
|
- type: map_at_3 |
|
value: 0.615 |
|
- type: map_at_5 |
|
value: 0.966 |
|
- type: mrr_at_1 |
|
value: 78.0 |
|
- type: mrr_at_10 |
|
value: 87.333 |
|
- type: mrr_at_100 |
|
value: 87.333 |
|
- type: mrr_at_1000 |
|
value: 87.333 |
|
- type: mrr_at_3 |
|
value: 86.333 |
|
- type: mrr_at_5 |
|
value: 87.333 |
|
- type: ndcg_at_1 |
|
value: 74.0 |
|
- type: ndcg_at_10 |
|
value: 69.12700000000001 |
|
- type: ndcg_at_100 |
|
value: 53.893 |
|
- type: ndcg_at_1000 |
|
value: 49.639 |
|
- type: ndcg_at_3 |
|
value: 74.654 |
|
- type: ndcg_at_5 |
|
value: 73.232 |
|
- type: precision_at_1 |
|
value: 78.0 |
|
- type: precision_at_10 |
|
value: 72.8 |
|
- type: precision_at_100 |
|
value: 55.42 |
|
- type: precision_at_1000 |
|
value: 21.73 |
|
- type: precision_at_3 |
|
value: 79.333 |
|
- type: precision_at_5 |
|
value: 77.2 |
|
- type: recall_at_1 |
|
value: 0.21 |
|
- type: recall_at_10 |
|
value: 1.9709999999999999 |
|
- type: recall_at_100 |
|
value: 13.555 |
|
- type: recall_at_1000 |
|
value: 46.961999999999996 |
|
- type: recall_at_3 |
|
value: 0.66 |
|
- type: recall_at_5 |
|
value: 1.052 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.456 |
|
- type: map_at_10 |
|
value: 9.426 |
|
- type: map_at_100 |
|
value: 16.066 |
|
- type: map_at_1000 |
|
value: 17.652 |
|
- type: map_at_3 |
|
value: 5.2459999999999996 |
|
- type: map_at_5 |
|
value: 6.5360000000000005 |
|
- type: mrr_at_1 |
|
value: 34.694 |
|
- type: mrr_at_10 |
|
value: 47.666 |
|
- type: mrr_at_100 |
|
value: 48.681999999999995 |
|
- type: mrr_at_1000 |
|
value: 48.681999999999995 |
|
- type: mrr_at_3 |
|
value: 43.878 |
|
- type: mrr_at_5 |
|
value: 46.224 |
|
- type: ndcg_at_1 |
|
value: 31.633 |
|
- type: ndcg_at_10 |
|
value: 23.454 |
|
- type: ndcg_at_100 |
|
value: 36.616 |
|
- type: ndcg_at_1000 |
|
value: 48.596000000000004 |
|
- type: ndcg_at_3 |
|
value: 28.267999999999997 |
|
- type: ndcg_at_5 |
|
value: 25.630999999999997 |
|
- type: precision_at_1 |
|
value: 34.694 |
|
- type: precision_at_10 |
|
value: 20.204 |
|
- type: precision_at_100 |
|
value: 7.754999999999999 |
|
- type: precision_at_1000 |
|
value: 1.5709999999999997 |
|
- type: precision_at_3 |
|
value: 29.252 |
|
- type: precision_at_5 |
|
value: 24.898 |
|
- type: recall_at_1 |
|
value: 2.456 |
|
- type: recall_at_10 |
|
value: 14.951 |
|
- type: recall_at_100 |
|
value: 48.399 |
|
- type: recall_at_1000 |
|
value: 85.077 |
|
- type: recall_at_3 |
|
value: 6.1370000000000005 |
|
- type: recall_at_5 |
|
value: 8.671 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 71.86240000000001 |
|
- type: ap |
|
value: 14.678570078747494 |
|
- type: f1 |
|
value: 55.295967793934445 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 59.17374080362195 |
|
- type: f1 |
|
value: 59.54410874861454 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 51.91227822485289 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.12523097097217 |
|
- type: cos_sim_ap |
|
value: 77.59606075943269 |
|
- type: cos_sim_f1 |
|
value: 71.11395646606915 |
|
- type: cos_sim_precision |
|
value: 69.07960199004975 |
|
- type: cos_sim_recall |
|
value: 73.27176781002639 |
|
- type: dot_accuracy |
|
value: 84.68736961316088 |
|
- type: dot_ap |
|
value: 68.47167450741459 |
|
- type: dot_f1 |
|
value: 64.42152354914874 |
|
- type: dot_precision |
|
value: 60.887949260042284 |
|
- type: dot_recall |
|
value: 68.3905013192612 |
|
- type: euclidean_accuracy |
|
value: 86.88084878106932 |
|
- type: euclidean_ap |
|
value: 77.27351204978599 |
|
- type: euclidean_f1 |
|
value: 70.99179716629381 |
|
- type: euclidean_precision |
|
value: 67.10526315789474 |
|
- type: euclidean_recall |
|
value: 75.35620052770449 |
|
- type: manhattan_accuracy |
|
value: 86.83316445133218 |
|
- type: manhattan_ap |
|
value: 77.21835357308716 |
|
- type: manhattan_f1 |
|
value: 71.05587004676349 |
|
- type: manhattan_precision |
|
value: 66.58210332103322 |
|
- type: manhattan_recall |
|
value: 76.17414248021109 |
|
- type: max_accuracy |
|
value: 87.12523097097217 |
|
- type: max_ap |
|
value: 77.59606075943269 |
|
- type: max_f1 |
|
value: 71.11395646606915 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.97232894787906 |
|
- type: cos_sim_ap |
|
value: 85.9613736469497 |
|
- type: cos_sim_f1 |
|
value: 78.40216655382532 |
|
- type: cos_sim_precision |
|
value: 72.97512437810946 |
|
- type: cos_sim_recall |
|
value: 84.70126270403449 |
|
- type: dot_accuracy |
|
value: 88.04866689952264 |
|
- type: dot_ap |
|
value: 83.15465089499936 |
|
- type: dot_f1 |
|
value: 76.32698287879329 |
|
- type: dot_precision |
|
value: 71.23223697378077 |
|
- type: dot_recall |
|
value: 82.20665229442562 |
|
- type: euclidean_accuracy |
|
value: 88.67543757519307 |
|
- type: euclidean_ap |
|
value: 85.4524355531532 |
|
- type: euclidean_f1 |
|
value: 77.78729106950081 |
|
- type: euclidean_precision |
|
value: 75.3009009009009 |
|
- type: euclidean_recall |
|
value: 80.44348629504158 |
|
- type: manhattan_accuracy |
|
value: 88.65991384328792 |
|
- type: manhattan_ap |
|
value: 85.43109069046837 |
|
- type: manhattan_f1 |
|
value: 77.72639551396425 |
|
- type: manhattan_precision |
|
value: 73.73402417962004 |
|
- type: manhattan_recall |
|
value: 82.17585463504774 |
|
- type: max_accuracy |
|
value: 88.97232894787906 |
|
- type: max_ap |
|
value: 85.9613736469497 |
|
- type: max_f1 |
|
value: 78.40216655382532 |
|
--- |
|
<h1 align="center">GIST Large Embedding v0</h1> |
|
|
|
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* |
|
|
|
The model is fine-tuned on top of the [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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 paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) |
|
|
|
|
|
# Data |
|
|
|
The dataset used is a compilation of the MEDI and MTEB Classification training datasets. 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-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. |
|
|
|
# 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 if the model is updated. |
|
|
|
model = SentenceTransformer("avsolatorio/GIST-large-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 = 171000 |
|
Contrastive loss temperature = 0.01 |
|
``` |
|
|
|
|
|
# Evaluation |
|
|
|
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. |
|
|
|
|
|
# Citation |
|
|
|
Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 |
|
|
|
``` |
|
@article{solatorio2024gistembed, |
|
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, |
|
author={Aivin V. Solatorio}, |
|
journal={arXiv preprint arXiv:2402.16829}, |
|
year={2024}, |
|
URL={https://arxiv.org/abs/2402.16829} |
|
eprint={2402.16829}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
# 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. |
