|
--- |
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tags: |
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- mteb |
|
- sentence-similarity |
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- sentence-transformers |
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- Sentence Transformers |
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model-index: |
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- name: gte-large |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 72.62686567164178 |
|
- type: ap |
|
value: 34.46944126809772 |
|
- type: f1 |
|
value: 66.23684353950857 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
|
config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 92.51805 |
|
- type: ap |
|
value: 89.49842783330848 |
|
- type: f1 |
|
value: 92.51112169431808 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 49.074 |
|
- type: f1 |
|
value: 48.44785682572955 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.077 |
|
- type: map_at_10 |
|
value: 48.153 |
|
- type: map_at_100 |
|
value: 48.963 |
|
- type: map_at_1000 |
|
value: 48.966 |
|
- type: map_at_3 |
|
value: 43.184 |
|
- type: map_at_5 |
|
value: 46.072 |
|
- type: mrr_at_1 |
|
value: 33.073 |
|
- type: mrr_at_10 |
|
value: 48.54 |
|
- type: mrr_at_100 |
|
value: 49.335 |
|
- type: mrr_at_1000 |
|
value: 49.338 |
|
- type: mrr_at_3 |
|
value: 43.563 |
|
- type: mrr_at_5 |
|
value: 46.383 |
|
- type: ndcg_at_1 |
|
value: 32.077 |
|
- type: ndcg_at_10 |
|
value: 57.158 |
|
- type: ndcg_at_100 |
|
value: 60.324999999999996 |
|
- type: ndcg_at_1000 |
|
value: 60.402 |
|
- type: ndcg_at_3 |
|
value: 46.934 |
|
- type: ndcg_at_5 |
|
value: 52.158 |
|
- type: precision_at_1 |
|
value: 32.077 |
|
- type: precision_at_10 |
|
value: 8.591999999999999 |
|
- type: precision_at_100 |
|
value: 0.991 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 19.275000000000002 |
|
- type: precision_at_5 |
|
value: 14.111 |
|
- type: recall_at_1 |
|
value: 32.077 |
|
- type: recall_at_10 |
|
value: 85.917 |
|
- type: recall_at_100 |
|
value: 99.075 |
|
- type: recall_at_1000 |
|
value: 99.644 |
|
- type: recall_at_3 |
|
value: 57.824 |
|
- type: recall_at_5 |
|
value: 70.555 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 48.619246083417295 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 43.3574067664688 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 63.06359661829253 |
|
- type: mrr |
|
value: 76.15596007562766 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 90.25407547368691 |
|
- type: cos_sim_spearman |
|
value: 88.65081514968477 |
|
- type: euclidean_pearson |
|
value: 88.14857116664494 |
|
- type: euclidean_spearman |
|
value: 88.50683596540692 |
|
- type: manhattan_pearson |
|
value: 87.9654797992225 |
|
- type: manhattan_spearman |
|
value: 88.21164851646908 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 86.05844155844157 |
|
- type: f1 |
|
value: 86.01555597681825 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 39.10510519739522 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 36.84689960264385 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.800000000000004 |
|
- type: map_at_10 |
|
value: 44.857 |
|
- type: map_at_100 |
|
value: 46.512 |
|
- type: map_at_1000 |
|
value: 46.635 |
|
- type: map_at_3 |
|
value: 41.062 |
|
- type: map_at_5 |
|
value: 43.126 |
|
- type: mrr_at_1 |
|
value: 39.628 |
|
- type: mrr_at_10 |
|
value: 50.879 |
|
- type: mrr_at_100 |
|
value: 51.605000000000004 |
|
- type: mrr_at_1000 |
|
value: 51.641000000000005 |
|
- type: mrr_at_3 |
|
value: 48.14 |
|
- type: mrr_at_5 |
|
value: 49.835 |
|
- type: ndcg_at_1 |
|
value: 39.628 |
|
- type: ndcg_at_10 |
|
value: 51.819 |
|
- type: ndcg_at_100 |
|
value: 57.318999999999996 |
|
- type: ndcg_at_1000 |
|
value: 58.955999999999996 |
|
- type: ndcg_at_3 |
|
value: 46.409 |
|
- type: ndcg_at_5 |
|
value: 48.825 |
|
- type: precision_at_1 |
|
value: 39.628 |
|
- type: precision_at_10 |
|
value: 10.072000000000001 |
|
- type: precision_at_100 |
|
value: 1.625 |
|
- type: precision_at_1000 |
|
value: 0.21 |
|
- type: precision_at_3 |
|
value: 22.556 |
|
- type: precision_at_5 |
|
value: 16.309 |
|
- type: recall_at_1 |
|
value: 32.800000000000004 |
|
- type: recall_at_10 |
|
value: 65.078 |
|
- type: recall_at_100 |
|
value: 87.491 |
|
- type: recall_at_1000 |
|
value: 97.514 |
|
- type: recall_at_3 |
|
value: 49.561 |
|
- type: recall_at_5 |
|
value: 56.135999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.614 |
|
- type: map_at_10 |
|
value: 43.578 |
|
- type: map_at_100 |
|
value: 44.897 |
|
- type: map_at_1000 |
|
value: 45.023 |
|
- type: map_at_3 |
|
value: 40.282000000000004 |
|
- type: map_at_5 |
|
value: 42.117 |
|
- type: mrr_at_1 |
|
value: 40.510000000000005 |
|
- type: mrr_at_10 |
|
value: 49.428 |
|
- type: mrr_at_100 |
|
value: 50.068999999999996 |
|
- type: mrr_at_1000 |
|
value: 50.111000000000004 |
|
- type: mrr_at_3 |
|
value: 47.176 |
|
- type: mrr_at_5 |
|
value: 48.583999999999996 |
|
- type: ndcg_at_1 |
|
value: 40.510000000000005 |
|
- type: ndcg_at_10 |
|
value: 49.478 |
|
- type: ndcg_at_100 |
|
value: 53.852 |
|
- type: ndcg_at_1000 |
|
value: 55.782 |
|
- type: ndcg_at_3 |
|
value: 45.091 |
|
- type: ndcg_at_5 |
|
value: 47.19 |
|
- type: precision_at_1 |
|
value: 40.510000000000005 |
|
- type: precision_at_10 |
|
value: 9.363000000000001 |
|
- type: precision_at_100 |
|
value: 1.51 |
|
- type: precision_at_1000 |
|
value: 0.196 |
|
- type: precision_at_3 |
|
value: 21.741 |
|
- type: precision_at_5 |
|
value: 15.465000000000002 |
|
- type: recall_at_1 |
|
value: 32.614 |
|
- type: recall_at_10 |
|
value: 59.782000000000004 |
|
- type: recall_at_100 |
|
value: 78.012 |
|
- type: recall_at_1000 |
|
value: 90.319 |
|
- type: recall_at_3 |
|
value: 46.825 |
|
- type: recall_at_5 |
|
value: 52.688 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.266000000000005 |
|
- type: map_at_10 |
|
value: 53.756 |
|
- type: map_at_100 |
|
value: 54.809 |
|
- type: map_at_1000 |
|
value: 54.855 |
|
- type: map_at_3 |
|
value: 50.073 |
|
- type: map_at_5 |
|
value: 52.293 |
|
- type: mrr_at_1 |
|
value: 46.332 |
|
- type: mrr_at_10 |
|
value: 57.116 |
|
- type: mrr_at_100 |
|
value: 57.767 |
|
- type: mrr_at_1000 |
|
value: 57.791000000000004 |
|
- type: mrr_at_3 |
|
value: 54.461999999999996 |
|
- type: mrr_at_5 |
|
value: 56.092 |
|
- type: ndcg_at_1 |
|
value: 46.332 |
|
- type: ndcg_at_10 |
|
value: 60.092 |
|
- type: ndcg_at_100 |
|
value: 64.034 |
|
- type: ndcg_at_1000 |
|
value: 64.937 |
|
- type: ndcg_at_3 |
|
value: 54.071000000000005 |
|
- type: ndcg_at_5 |
|
value: 57.254000000000005 |
|
- type: precision_at_1 |
|
value: 46.332 |
|
- type: precision_at_10 |
|
value: 9.799 |
|
- type: precision_at_100 |
|
value: 1.278 |
|
- type: precision_at_1000 |
|
value: 0.13899999999999998 |
|
- type: precision_at_3 |
|
value: 24.368000000000002 |
|
- type: precision_at_5 |
|
value: 16.89 |
|
- type: recall_at_1 |
|
value: 40.266000000000005 |
|
- type: recall_at_10 |
|
value: 75.41499999999999 |
|
- type: recall_at_100 |
|
value: 92.01700000000001 |
|
- type: recall_at_1000 |
|
value: 98.379 |
|
- type: recall_at_3 |
|
value: 59.476 |
|
- type: recall_at_5 |
|
value: 67.297 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.589 |
|
- type: map_at_10 |
|
value: 37.755 |
|
- type: map_at_100 |
|
value: 38.881 |
|
- type: map_at_1000 |
|
value: 38.954 |
|
- type: map_at_3 |
|
value: 34.759 |
|
- type: map_at_5 |
|
value: 36.544 |
|
- type: mrr_at_1 |
|
value: 30.734 |
|
- type: mrr_at_10 |
|
value: 39.742 |
|
- type: mrr_at_100 |
|
value: 40.774 |
|
- type: mrr_at_1000 |
|
value: 40.824 |
|
- type: mrr_at_3 |
|
value: 37.137 |
|
- type: mrr_at_5 |
|
value: 38.719 |
|
- type: ndcg_at_1 |
|
value: 30.734 |
|
- type: ndcg_at_10 |
|
value: 42.978 |
|
- type: ndcg_at_100 |
|
value: 48.309000000000005 |
|
- type: ndcg_at_1000 |
|
value: 50.068 |
|
- type: ndcg_at_3 |
|
value: 37.361 |
|
- type: ndcg_at_5 |
|
value: 40.268 |
|
- type: precision_at_1 |
|
value: 30.734 |
|
- type: precision_at_10 |
|
value: 6.565 |
|
- type: precision_at_100 |
|
value: 0.964 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 15.744 |
|
- type: precision_at_5 |
|
value: 11.096 |
|
- type: recall_at_1 |
|
value: 28.589 |
|
- type: recall_at_10 |
|
value: 57.126999999999995 |
|
- type: recall_at_100 |
|
value: 81.051 |
|
- type: recall_at_1000 |
|
value: 94.027 |
|
- type: recall_at_3 |
|
value: 42.045 |
|
- type: recall_at_5 |
|
value: 49.019 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.5 |
|
- type: map_at_10 |
|
value: 27.950999999999997 |
|
- type: map_at_100 |
|
value: 29.186 |
|
- type: map_at_1000 |
|
value: 29.298000000000002 |
|
- type: map_at_3 |
|
value: 25.141000000000002 |
|
- type: map_at_5 |
|
value: 26.848 |
|
- type: mrr_at_1 |
|
value: 22.637 |
|
- type: mrr_at_10 |
|
value: 32.572 |
|
- type: mrr_at_100 |
|
value: 33.472 |
|
- type: mrr_at_1000 |
|
value: 33.533 |
|
- type: mrr_at_3 |
|
value: 29.747 |
|
- type: mrr_at_5 |
|
value: 31.482 |
|
- type: ndcg_at_1 |
|
value: 22.637 |
|
- type: ndcg_at_10 |
|
value: 33.73 |
|
- type: ndcg_at_100 |
|
value: 39.568 |
|
- type: ndcg_at_1000 |
|
value: 42.201 |
|
- type: ndcg_at_3 |
|
value: 28.505999999999997 |
|
- type: ndcg_at_5 |
|
value: 31.255 |
|
- type: precision_at_1 |
|
value: 22.637 |
|
- type: precision_at_10 |
|
value: 6.281000000000001 |
|
- type: precision_at_100 |
|
value: 1.073 |
|
- type: precision_at_1000 |
|
value: 0.14300000000000002 |
|
- type: precision_at_3 |
|
value: 13.847000000000001 |
|
- type: precision_at_5 |
|
value: 10.224 |
|
- type: recall_at_1 |
|
value: 18.5 |
|
- type: recall_at_10 |
|
value: 46.744 |
|
- type: recall_at_100 |
|
value: 72.072 |
|
- type: recall_at_1000 |
|
value: 91.03999999999999 |
|
- type: recall_at_3 |
|
value: 32.551 |
|
- type: recall_at_5 |
|
value: 39.533 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.602 |
|
- type: map_at_10 |
|
value: 42.18 |
|
- type: map_at_100 |
|
value: 43.6 |
|
- type: map_at_1000 |
|
value: 43.704 |
|
- type: map_at_3 |
|
value: 38.413000000000004 |
|
- type: map_at_5 |
|
value: 40.626 |
|
- type: mrr_at_1 |
|
value: 37.344 |
|
- type: mrr_at_10 |
|
value: 47.638000000000005 |
|
- type: mrr_at_100 |
|
value: 48.485 |
|
- type: mrr_at_1000 |
|
value: 48.52 |
|
- type: mrr_at_3 |
|
value: 44.867000000000004 |
|
- type: mrr_at_5 |
|
value: 46.566 |
|
- type: ndcg_at_1 |
|
value: 37.344 |
|
- type: ndcg_at_10 |
|
value: 48.632 |
|
- type: ndcg_at_100 |
|
value: 54.215 |
|
- type: ndcg_at_1000 |
|
value: 55.981 |
|
- type: ndcg_at_3 |
|
value: 42.681999999999995 |
|
- type: ndcg_at_5 |
|
value: 45.732 |
|
- type: precision_at_1 |
|
value: 37.344 |
|
- type: precision_at_10 |
|
value: 8.932 |
|
- type: precision_at_100 |
|
value: 1.376 |
|
- type: precision_at_1000 |
|
value: 0.17099999999999999 |
|
- type: precision_at_3 |
|
value: 20.276 |
|
- type: precision_at_5 |
|
value: 14.726 |
|
- type: recall_at_1 |
|
value: 30.602 |
|
- type: recall_at_10 |
|
value: 62.273 |
|
- type: recall_at_100 |
|
value: 85.12100000000001 |
|
- type: recall_at_1000 |
|
value: 96.439 |
|
- type: recall_at_3 |
|
value: 45.848 |
|
- type: recall_at_5 |
|
value: 53.615 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.952 |
|
- type: map_at_10 |
|
value: 35.177 |
|
- type: map_at_100 |
|
value: 36.59 |
|
- type: map_at_1000 |
|
value: 36.703 |
|
- type: map_at_3 |
|
value: 31.261 |
|
- type: map_at_5 |
|
value: 33.222 |
|
- type: mrr_at_1 |
|
value: 29.337999999999997 |
|
- type: mrr_at_10 |
|
value: 40.152 |
|
- type: mrr_at_100 |
|
value: 40.963 |
|
- type: mrr_at_1000 |
|
value: 41.016999999999996 |
|
- type: mrr_at_3 |
|
value: 36.91 |
|
- type: mrr_at_5 |
|
value: 38.685 |
|
- type: ndcg_at_1 |
|
value: 29.337999999999997 |
|
- type: ndcg_at_10 |
|
value: 41.994 |
|
- type: ndcg_at_100 |
|
value: 47.587 |
|
- type: ndcg_at_1000 |
|
value: 49.791000000000004 |
|
- type: ndcg_at_3 |
|
value: 35.27 |
|
- type: ndcg_at_5 |
|
value: 38.042 |
|
- type: precision_at_1 |
|
value: 29.337999999999997 |
|
- type: precision_at_10 |
|
value: 8.276 |
|
- type: precision_at_100 |
|
value: 1.276 |
|
- type: precision_at_1000 |
|
value: 0.164 |
|
- type: precision_at_3 |
|
value: 17.161 |
|
- type: precision_at_5 |
|
value: 12.671 |
|
- type: recall_at_1 |
|
value: 23.952 |
|
- type: recall_at_10 |
|
value: 57.267 |
|
- type: recall_at_100 |
|
value: 80.886 |
|
- type: recall_at_1000 |
|
value: 95.611 |
|
- type: recall_at_3 |
|
value: 38.622 |
|
- type: recall_at_5 |
|
value: 45.811 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.092083333333335 |
|
- type: map_at_10 |
|
value: 37.2925 |
|
- type: map_at_100 |
|
value: 38.57041666666666 |
|
- type: map_at_1000 |
|
value: 38.68141666666667 |
|
- type: map_at_3 |
|
value: 34.080000000000005 |
|
- type: map_at_5 |
|
value: 35.89958333333333 |
|
- type: mrr_at_1 |
|
value: 31.94758333333333 |
|
- type: mrr_at_10 |
|
value: 41.51049999999999 |
|
- type: mrr_at_100 |
|
value: 42.36099999999999 |
|
- type: mrr_at_1000 |
|
value: 42.4125 |
|
- type: mrr_at_3 |
|
value: 38.849583333333335 |
|
- type: mrr_at_5 |
|
value: 40.448249999999994 |
|
- type: ndcg_at_1 |
|
value: 31.94758333333333 |
|
- type: ndcg_at_10 |
|
value: 43.17633333333333 |
|
- type: ndcg_at_100 |
|
value: 48.45241666666668 |
|
- type: ndcg_at_1000 |
|
value: 50.513999999999996 |
|
- type: ndcg_at_3 |
|
value: 37.75216666666667 |
|
- type: ndcg_at_5 |
|
value: 40.393833333333326 |
|
- type: precision_at_1 |
|
value: 31.94758333333333 |
|
- type: precision_at_10 |
|
value: 7.688916666666666 |
|
- type: precision_at_100 |
|
value: 1.2250833333333333 |
|
- type: precision_at_1000 |
|
value: 0.1595 |
|
- type: precision_at_3 |
|
value: 17.465999999999998 |
|
- type: precision_at_5 |
|
value: 12.548083333333333 |
|
- type: recall_at_1 |
|
value: 27.092083333333335 |
|
- type: recall_at_10 |
|
value: 56.286583333333326 |
|
- type: recall_at_100 |
|
value: 79.09033333333333 |
|
- type: recall_at_1000 |
|
value: 93.27483333333335 |
|
- type: recall_at_3 |
|
value: 41.35325 |
|
- type: recall_at_5 |
|
value: 48.072750000000006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.825 |
|
- type: map_at_10 |
|
value: 33.723 |
|
- type: map_at_100 |
|
value: 34.74 |
|
- type: map_at_1000 |
|
value: 34.824 |
|
- type: map_at_3 |
|
value: 31.369000000000003 |
|
- type: map_at_5 |
|
value: 32.533 |
|
- type: mrr_at_1 |
|
value: 29.293999999999997 |
|
- type: mrr_at_10 |
|
value: 36.84 |
|
- type: mrr_at_100 |
|
value: 37.681 |
|
- type: mrr_at_1000 |
|
value: 37.742 |
|
- type: mrr_at_3 |
|
value: 34.79 |
|
- type: mrr_at_5 |
|
value: 35.872 |
|
- type: ndcg_at_1 |
|
value: 29.293999999999997 |
|
- type: ndcg_at_10 |
|
value: 38.385999999999996 |
|
- type: ndcg_at_100 |
|
value: 43.327 |
|
- type: ndcg_at_1000 |
|
value: 45.53 |
|
- type: ndcg_at_3 |
|
value: 33.985 |
|
- type: ndcg_at_5 |
|
value: 35.817 |
|
- type: precision_at_1 |
|
value: 29.293999999999997 |
|
- type: precision_at_10 |
|
value: 6.12 |
|
- type: precision_at_100 |
|
value: 0.9329999999999999 |
|
- type: precision_at_1000 |
|
value: 0.11900000000000001 |
|
- type: precision_at_3 |
|
value: 14.621999999999998 |
|
- type: precision_at_5 |
|
value: 10.030999999999999 |
|
- type: recall_at_1 |
|
value: 25.825 |
|
- type: recall_at_10 |
|
value: 49.647000000000006 |
|
- type: recall_at_100 |
|
value: 72.32300000000001 |
|
- type: recall_at_1000 |
|
value: 88.62400000000001 |
|
- type: recall_at_3 |
|
value: 37.366 |
|
- type: recall_at_5 |
|
value: 41.957 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.139 |
|
- type: map_at_10 |
|
value: 26.107000000000003 |
|
- type: map_at_100 |
|
value: 27.406999999999996 |
|
- type: map_at_1000 |
|
value: 27.535999999999998 |
|
- type: map_at_3 |
|
value: 23.445 |
|
- type: map_at_5 |
|
value: 24.916 |
|
- type: mrr_at_1 |
|
value: 21.817 |
|
- type: mrr_at_10 |
|
value: 29.99 |
|
- type: mrr_at_100 |
|
value: 31.052000000000003 |
|
- type: mrr_at_1000 |
|
value: 31.128 |
|
- type: mrr_at_3 |
|
value: 27.627000000000002 |
|
- type: mrr_at_5 |
|
value: 29.005 |
|
- type: ndcg_at_1 |
|
value: 21.817 |
|
- type: ndcg_at_10 |
|
value: 31.135 |
|
- type: ndcg_at_100 |
|
value: 37.108000000000004 |
|
- type: ndcg_at_1000 |
|
value: 39.965 |
|
- type: ndcg_at_3 |
|
value: 26.439 |
|
- type: ndcg_at_5 |
|
value: 28.655 |
|
- type: precision_at_1 |
|
value: 21.817 |
|
- type: precision_at_10 |
|
value: 5.757000000000001 |
|
- type: precision_at_100 |
|
value: 1.036 |
|
- type: precision_at_1000 |
|
value: 0.147 |
|
- type: precision_at_3 |
|
value: 12.537 |
|
- type: precision_at_5 |
|
value: 9.229 |
|
- type: recall_at_1 |
|
value: 18.139 |
|
- type: recall_at_10 |
|
value: 42.272999999999996 |
|
- type: recall_at_100 |
|
value: 68.657 |
|
- type: recall_at_1000 |
|
value: 88.93799999999999 |
|
- type: recall_at_3 |
|
value: 29.266 |
|
- type: recall_at_5 |
|
value: 34.892 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.755000000000003 |
|
- type: map_at_10 |
|
value: 37.384 |
|
- type: map_at_100 |
|
value: 38.56 |
|
- type: map_at_1000 |
|
value: 38.655 |
|
- type: map_at_3 |
|
value: 34.214 |
|
- type: map_at_5 |
|
value: 35.96 |
|
- type: mrr_at_1 |
|
value: 32.369 |
|
- type: mrr_at_10 |
|
value: 41.625 |
|
- type: mrr_at_100 |
|
value: 42.449 |
|
- type: mrr_at_1000 |
|
value: 42.502 |
|
- type: mrr_at_3 |
|
value: 38.899 |
|
- type: mrr_at_5 |
|
value: 40.489999999999995 |
|
- type: ndcg_at_1 |
|
value: 32.369 |
|
- type: ndcg_at_10 |
|
value: 43.287 |
|
- type: ndcg_at_100 |
|
value: 48.504999999999995 |
|
- type: ndcg_at_1000 |
|
value: 50.552 |
|
- type: ndcg_at_3 |
|
value: 37.549 |
|
- type: ndcg_at_5 |
|
value: 40.204 |
|
- type: precision_at_1 |
|
value: 32.369 |
|
- type: precision_at_10 |
|
value: 7.425 |
|
- type: precision_at_100 |
|
value: 1.134 |
|
- type: precision_at_1000 |
|
value: 0.14200000000000002 |
|
- type: precision_at_3 |
|
value: 17.102 |
|
- type: precision_at_5 |
|
value: 12.107999999999999 |
|
- type: recall_at_1 |
|
value: 27.755000000000003 |
|
- type: recall_at_10 |
|
value: 57.071000000000005 |
|
- type: recall_at_100 |
|
value: 79.456 |
|
- type: recall_at_1000 |
|
value: 93.54299999999999 |
|
- type: recall_at_3 |
|
value: 41.298 |
|
- type: recall_at_5 |
|
value: 48.037 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.855 |
|
- type: map_at_10 |
|
value: 34.53 |
|
- type: map_at_100 |
|
value: 36.167 |
|
- type: map_at_1000 |
|
value: 36.394999999999996 |
|
- type: map_at_3 |
|
value: 31.037 |
|
- type: map_at_5 |
|
value: 33.119 |
|
- type: mrr_at_1 |
|
value: 30.631999999999998 |
|
- type: mrr_at_10 |
|
value: 39.763999999999996 |
|
- type: mrr_at_100 |
|
value: 40.77 |
|
- type: mrr_at_1000 |
|
value: 40.826 |
|
- type: mrr_at_3 |
|
value: 36.495 |
|
- type: mrr_at_5 |
|
value: 38.561 |
|
- type: ndcg_at_1 |
|
value: 30.631999999999998 |
|
- type: ndcg_at_10 |
|
value: 40.942 |
|
- type: ndcg_at_100 |
|
value: 47.07 |
|
- type: ndcg_at_1000 |
|
value: 49.363 |
|
- type: ndcg_at_3 |
|
value: 35.038000000000004 |
|
- type: ndcg_at_5 |
|
value: 38.161 |
|
- type: precision_at_1 |
|
value: 30.631999999999998 |
|
- type: precision_at_10 |
|
value: 7.983999999999999 |
|
- type: precision_at_100 |
|
value: 1.6070000000000002 |
|
- type: precision_at_1000 |
|
value: 0.246 |
|
- type: precision_at_3 |
|
value: 16.206 |
|
- type: precision_at_5 |
|
value: 12.253 |
|
- type: recall_at_1 |
|
value: 24.855 |
|
- type: recall_at_10 |
|
value: 53.291999999999994 |
|
- type: recall_at_100 |
|
value: 80.283 |
|
- type: recall_at_1000 |
|
value: 94.309 |
|
- type: recall_at_3 |
|
value: 37.257 |
|
- type: recall_at_5 |
|
value: 45.282 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.208 |
|
- type: map_at_10 |
|
value: 30.512 |
|
- type: map_at_100 |
|
value: 31.496000000000002 |
|
- type: map_at_1000 |
|
value: 31.595000000000002 |
|
- type: map_at_3 |
|
value: 27.904 |
|
- type: map_at_5 |
|
value: 29.491 |
|
- type: mrr_at_1 |
|
value: 22.736 |
|
- type: mrr_at_10 |
|
value: 32.379999999999995 |
|
- type: mrr_at_100 |
|
value: 33.245000000000005 |
|
- type: mrr_at_1000 |
|
value: 33.315 |
|
- type: mrr_at_3 |
|
value: 29.945 |
|
- type: mrr_at_5 |
|
value: 31.488 |
|
- type: ndcg_at_1 |
|
value: 22.736 |
|
- type: ndcg_at_10 |
|
value: 35.643 |
|
- type: ndcg_at_100 |
|
value: 40.535 |
|
- type: ndcg_at_1000 |
|
value: 43.042 |
|
- type: ndcg_at_3 |
|
value: 30.625000000000004 |
|
- type: ndcg_at_5 |
|
value: 33.323 |
|
- type: precision_at_1 |
|
value: 22.736 |
|
- type: precision_at_10 |
|
value: 5.6930000000000005 |
|
- type: precision_at_100 |
|
value: 0.889 |
|
- type: precision_at_1000 |
|
value: 0.122 |
|
- type: precision_at_3 |
|
value: 13.431999999999999 |
|
- type: precision_at_5 |
|
value: 9.575 |
|
- type: recall_at_1 |
|
value: 21.208 |
|
- type: recall_at_10 |
|
value: 49.47 |
|
- type: recall_at_100 |
|
value: 71.71499999999999 |
|
- type: recall_at_1000 |
|
value: 90.55499999999999 |
|
- type: recall_at_3 |
|
value: 36.124 |
|
- type: recall_at_5 |
|
value: 42.606 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 11.363 |
|
- type: map_at_10 |
|
value: 20.312 |
|
- type: map_at_100 |
|
value: 22.225 |
|
- type: map_at_1000 |
|
value: 22.411 |
|
- type: map_at_3 |
|
value: 16.68 |
|
- type: map_at_5 |
|
value: 18.608 |
|
- type: mrr_at_1 |
|
value: 25.537 |
|
- type: mrr_at_10 |
|
value: 37.933 |
|
- type: mrr_at_100 |
|
value: 38.875 |
|
- type: mrr_at_1000 |
|
value: 38.911 |
|
- type: mrr_at_3 |
|
value: 34.387 |
|
- type: mrr_at_5 |
|
value: 36.51 |
|
- type: ndcg_at_1 |
|
value: 25.537 |
|
- type: ndcg_at_10 |
|
value: 28.82 |
|
- type: ndcg_at_100 |
|
value: 36.341 |
|
- type: ndcg_at_1000 |
|
value: 39.615 |
|
- type: ndcg_at_3 |
|
value: 23.01 |
|
- type: ndcg_at_5 |
|
value: 25.269000000000002 |
|
- type: precision_at_1 |
|
value: 25.537 |
|
- type: precision_at_10 |
|
value: 9.153 |
|
- type: precision_at_100 |
|
value: 1.7319999999999998 |
|
- type: precision_at_1000 |
|
value: 0.234 |
|
- type: precision_at_3 |
|
value: 17.22 |
|
- type: precision_at_5 |
|
value: 13.629 |
|
- type: recall_at_1 |
|
value: 11.363 |
|
- type: recall_at_10 |
|
value: 35.382999999999996 |
|
- type: recall_at_100 |
|
value: 61.367000000000004 |
|
- type: recall_at_1000 |
|
value: 79.699 |
|
- type: recall_at_3 |
|
value: 21.495 |
|
- type: recall_at_5 |
|
value: 27.42 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.65 |
|
- type: map_at_10 |
|
value: 20.742 |
|
- type: map_at_100 |
|
value: 29.614 |
|
- type: map_at_1000 |
|
value: 31.373 |
|
- type: map_at_3 |
|
value: 14.667 |
|
- type: map_at_5 |
|
value: 17.186 |
|
- type: mrr_at_1 |
|
value: 69.75 |
|
- type: mrr_at_10 |
|
value: 76.762 |
|
- type: mrr_at_100 |
|
value: 77.171 |
|
- type: mrr_at_1000 |
|
value: 77.179 |
|
- type: mrr_at_3 |
|
value: 75.125 |
|
- type: mrr_at_5 |
|
value: 76.287 |
|
- type: ndcg_at_1 |
|
value: 57.62500000000001 |
|
- type: ndcg_at_10 |
|
value: 42.370999999999995 |
|
- type: ndcg_at_100 |
|
value: 47.897 |
|
- type: ndcg_at_1000 |
|
value: 55.393 |
|
- type: ndcg_at_3 |
|
value: 46.317 |
|
- type: ndcg_at_5 |
|
value: 43.906 |
|
- type: precision_at_1 |
|
value: 69.75 |
|
- type: precision_at_10 |
|
value: 33.95 |
|
- type: precision_at_100 |
|
value: 10.885 |
|
- type: precision_at_1000 |
|
value: 2.2239999999999998 |
|
- type: precision_at_3 |
|
value: 49.75 |
|
- type: precision_at_5 |
|
value: 42.3 |
|
- type: recall_at_1 |
|
value: 9.65 |
|
- type: recall_at_10 |
|
value: 26.117 |
|
- type: recall_at_100 |
|
value: 55.084 |
|
- type: recall_at_1000 |
|
value: 78.62400000000001 |
|
- type: recall_at_3 |
|
value: 15.823 |
|
- type: recall_at_5 |
|
value: 19.652 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 47.885 |
|
- type: f1 |
|
value: 42.99567641346983 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.97 |
|
- type: map_at_10 |
|
value: 80.34599999999999 |
|
- type: map_at_100 |
|
value: 80.571 |
|
- type: map_at_1000 |
|
value: 80.584 |
|
- type: map_at_3 |
|
value: 79.279 |
|
- type: map_at_5 |
|
value: 79.94 |
|
- type: mrr_at_1 |
|
value: 76.613 |
|
- type: mrr_at_10 |
|
value: 85.15700000000001 |
|
- type: mrr_at_100 |
|
value: 85.249 |
|
- type: mrr_at_1000 |
|
value: 85.252 |
|
- type: mrr_at_3 |
|
value: 84.33800000000001 |
|
- type: mrr_at_5 |
|
value: 84.89 |
|
- type: ndcg_at_1 |
|
value: 76.613 |
|
- type: ndcg_at_10 |
|
value: 84.53399999999999 |
|
- type: ndcg_at_100 |
|
value: 85.359 |
|
- type: ndcg_at_1000 |
|
value: 85.607 |
|
- type: ndcg_at_3 |
|
value: 82.76599999999999 |
|
- type: ndcg_at_5 |
|
value: 83.736 |
|
- type: precision_at_1 |
|
value: 76.613 |
|
- type: precision_at_10 |
|
value: 10.206 |
|
- type: precision_at_100 |
|
value: 1.083 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 31.913000000000004 |
|
- type: precision_at_5 |
|
value: 19.769000000000002 |
|
- type: recall_at_1 |
|
value: 70.97 |
|
- type: recall_at_10 |
|
value: 92.674 |
|
- type: recall_at_100 |
|
value: 95.985 |
|
- type: recall_at_1000 |
|
value: 97.57000000000001 |
|
- type: recall_at_3 |
|
value: 87.742 |
|
- type: recall_at_5 |
|
value: 90.28 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.494 |
|
- type: map_at_10 |
|
value: 36.491 |
|
- type: map_at_100 |
|
value: 38.550000000000004 |
|
- type: map_at_1000 |
|
value: 38.726 |
|
- type: map_at_3 |
|
value: 31.807000000000002 |
|
- type: map_at_5 |
|
value: 34.299 |
|
- type: mrr_at_1 |
|
value: 44.907000000000004 |
|
- type: mrr_at_10 |
|
value: 53.146 |
|
- type: mrr_at_100 |
|
value: 54.013999999999996 |
|
- type: mrr_at_1000 |
|
value: 54.044000000000004 |
|
- type: mrr_at_3 |
|
value: 50.952 |
|
- type: mrr_at_5 |
|
value: 52.124 |
|
- type: ndcg_at_1 |
|
value: 44.907000000000004 |
|
- type: ndcg_at_10 |
|
value: 44.499 |
|
- type: ndcg_at_100 |
|
value: 51.629000000000005 |
|
- type: ndcg_at_1000 |
|
value: 54.367 |
|
- type: ndcg_at_3 |
|
value: 40.900999999999996 |
|
- type: ndcg_at_5 |
|
value: 41.737 |
|
- type: precision_at_1 |
|
value: 44.907000000000004 |
|
- type: precision_at_10 |
|
value: 12.346 |
|
- type: precision_at_100 |
|
value: 1.974 |
|
- type: precision_at_1000 |
|
value: 0.246 |
|
- type: precision_at_3 |
|
value: 27.366 |
|
- type: precision_at_5 |
|
value: 19.846 |
|
- type: recall_at_1 |
|
value: 22.494 |
|
- type: recall_at_10 |
|
value: 51.156 |
|
- type: recall_at_100 |
|
value: 77.11200000000001 |
|
- type: recall_at_1000 |
|
value: 93.44 |
|
- type: recall_at_3 |
|
value: 36.574 |
|
- type: recall_at_5 |
|
value: 42.361 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 38.568999999999996 |
|
- type: map_at_10 |
|
value: 58.485 |
|
- type: map_at_100 |
|
value: 59.358999999999995 |
|
- type: map_at_1000 |
|
value: 59.429 |
|
- type: map_at_3 |
|
value: 55.217000000000006 |
|
- type: map_at_5 |
|
value: 57.236 |
|
- type: mrr_at_1 |
|
value: 77.137 |
|
- type: mrr_at_10 |
|
value: 82.829 |
|
- type: mrr_at_100 |
|
value: 83.04599999999999 |
|
- type: mrr_at_1000 |
|
value: 83.05399999999999 |
|
- type: mrr_at_3 |
|
value: 81.904 |
|
- type: mrr_at_5 |
|
value: 82.50800000000001 |
|
- type: ndcg_at_1 |
|
value: 77.137 |
|
- type: ndcg_at_10 |
|
value: 67.156 |
|
- type: ndcg_at_100 |
|
value: 70.298 |
|
- type: ndcg_at_1000 |
|
value: 71.65700000000001 |
|
- type: ndcg_at_3 |
|
value: 62.535 |
|
- type: ndcg_at_5 |
|
value: 65.095 |
|
- type: precision_at_1 |
|
value: 77.137 |
|
- type: precision_at_10 |
|
value: 13.911999999999999 |
|
- type: precision_at_100 |
|
value: 1.6389999999999998 |
|
- type: precision_at_1000 |
|
value: 0.182 |
|
- type: precision_at_3 |
|
value: 39.572 |
|
- type: precision_at_5 |
|
value: 25.766 |
|
- type: recall_at_1 |
|
value: 38.568999999999996 |
|
- type: recall_at_10 |
|
value: 69.56099999999999 |
|
- type: recall_at_100 |
|
value: 81.931 |
|
- type: recall_at_1000 |
|
value: 90.91799999999999 |
|
- type: recall_at_3 |
|
value: 59.358999999999995 |
|
- type: recall_at_5 |
|
value: 64.416 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 88.45600000000002 |
|
- type: ap |
|
value: 84.09725115338568 |
|
- type: f1 |
|
value: 88.41874909080512 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.404999999999998 |
|
- type: map_at_10 |
|
value: 33.921 |
|
- type: map_at_100 |
|
value: 35.116 |
|
- type: map_at_1000 |
|
value: 35.164 |
|
- type: map_at_3 |
|
value: 30.043999999999997 |
|
- type: map_at_5 |
|
value: 32.327 |
|
- type: mrr_at_1 |
|
value: 21.977 |
|
- type: mrr_at_10 |
|
value: 34.505 |
|
- type: mrr_at_100 |
|
value: 35.638999999999996 |
|
- type: mrr_at_1000 |
|
value: 35.68 |
|
- type: mrr_at_3 |
|
value: 30.703999999999997 |
|
- type: mrr_at_5 |
|
value: 32.96 |
|
- type: ndcg_at_1 |
|
value: 21.963 |
|
- type: ndcg_at_10 |
|
value: 40.859 |
|
- type: ndcg_at_100 |
|
value: 46.614 |
|
- type: ndcg_at_1000 |
|
value: 47.789 |
|
- type: ndcg_at_3 |
|
value: 33.007999999999996 |
|
- type: ndcg_at_5 |
|
value: 37.084 |
|
- type: precision_at_1 |
|
value: 21.963 |
|
- type: precision_at_10 |
|
value: 6.493 |
|
- type: precision_at_100 |
|
value: 0.938 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.155000000000001 |
|
- type: precision_at_5 |
|
value: 10.544 |
|
- type: recall_at_1 |
|
value: 21.404999999999998 |
|
- type: recall_at_10 |
|
value: 62.175000000000004 |
|
- type: recall_at_100 |
|
value: 88.786 |
|
- type: recall_at_1000 |
|
value: 97.738 |
|
- type: recall_at_3 |
|
value: 40.925 |
|
- type: recall_at_5 |
|
value: 50.722 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.50661194710442 |
|
- type: f1 |
|
value: 93.30311193153668 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 73.24669402644778 |
|
- type: f1 |
|
value: 54.23122108002977 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 72.61936785474109 |
|
- type: f1 |
|
value: 70.52644941025565 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 76.76529926025555 |
|
- type: f1 |
|
value: 77.26872729322514 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 33.39450293021839 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 31.757796879839294 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 32.62512146657428 |
|
- type: mrr |
|
value: 33.84624322066173 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.462 |
|
- type: map_at_10 |
|
value: 14.947 |
|
- type: map_at_100 |
|
value: 19.344 |
|
- type: map_at_1000 |
|
value: 20.933 |
|
- type: map_at_3 |
|
value: 10.761999999999999 |
|
- type: map_at_5 |
|
value: 12.744 |
|
- type: mrr_at_1 |
|
value: 47.988 |
|
- type: mrr_at_10 |
|
value: 57.365 |
|
- type: mrr_at_100 |
|
value: 57.931 |
|
- type: mrr_at_1000 |
|
value: 57.96 |
|
- type: mrr_at_3 |
|
value: 54.85 |
|
- type: mrr_at_5 |
|
value: 56.569 |
|
- type: ndcg_at_1 |
|
value: 46.129999999999995 |
|
- type: ndcg_at_10 |
|
value: 38.173 |
|
- type: ndcg_at_100 |
|
value: 35.983 |
|
- type: ndcg_at_1000 |
|
value: 44.507000000000005 |
|
- type: ndcg_at_3 |
|
value: 42.495 |
|
- type: ndcg_at_5 |
|
value: 41.019 |
|
- type: precision_at_1 |
|
value: 47.678 |
|
- type: precision_at_10 |
|
value: 28.731 |
|
- type: precision_at_100 |
|
value: 9.232 |
|
- type: precision_at_1000 |
|
value: 2.202 |
|
- type: precision_at_3 |
|
value: 39.628 |
|
- type: precision_at_5 |
|
value: 35.851 |
|
- type: recall_at_1 |
|
value: 6.462 |
|
- type: recall_at_10 |
|
value: 18.968 |
|
- type: recall_at_100 |
|
value: 37.131 |
|
- type: recall_at_1000 |
|
value: 67.956 |
|
- type: recall_at_3 |
|
value: 11.905000000000001 |
|
- type: recall_at_5 |
|
value: 15.097 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.335 |
|
- type: map_at_10 |
|
value: 46.611999999999995 |
|
- type: map_at_100 |
|
value: 47.632000000000005 |
|
- type: map_at_1000 |
|
value: 47.661 |
|
- type: map_at_3 |
|
value: 41.876999999999995 |
|
- type: map_at_5 |
|
value: 44.799 |
|
- type: mrr_at_1 |
|
value: 34.125 |
|
- type: mrr_at_10 |
|
value: 49.01 |
|
- type: mrr_at_100 |
|
value: 49.75 |
|
- type: mrr_at_1000 |
|
value: 49.768 |
|
- type: mrr_at_3 |
|
value: 45.153 |
|
- type: mrr_at_5 |
|
value: 47.589999999999996 |
|
- type: ndcg_at_1 |
|
value: 34.125 |
|
- type: ndcg_at_10 |
|
value: 54.777 |
|
- type: ndcg_at_100 |
|
value: 58.914 |
|
- type: ndcg_at_1000 |
|
value: 59.521 |
|
- type: ndcg_at_3 |
|
value: 46.015 |
|
- type: ndcg_at_5 |
|
value: 50.861000000000004 |
|
- type: precision_at_1 |
|
value: 34.125 |
|
- type: precision_at_10 |
|
value: 9.166 |
|
- type: precision_at_100 |
|
value: 1.149 |
|
- type: precision_at_1000 |
|
value: 0.121 |
|
- type: precision_at_3 |
|
value: 21.147 |
|
- type: precision_at_5 |
|
value: 15.469 |
|
- type: recall_at_1 |
|
value: 30.335 |
|
- type: recall_at_10 |
|
value: 77.194 |
|
- type: recall_at_100 |
|
value: 94.812 |
|
- type: recall_at_1000 |
|
value: 99.247 |
|
- type: recall_at_3 |
|
value: 54.681000000000004 |
|
- type: recall_at_5 |
|
value: 65.86800000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.62 |
|
- type: map_at_10 |
|
value: 84.536 |
|
- type: map_at_100 |
|
value: 85.167 |
|
- type: map_at_1000 |
|
value: 85.184 |
|
- type: map_at_3 |
|
value: 81.607 |
|
- type: map_at_5 |
|
value: 83.423 |
|
- type: mrr_at_1 |
|
value: 81.36 |
|
- type: mrr_at_10 |
|
value: 87.506 |
|
- type: mrr_at_100 |
|
value: 87.601 |
|
- type: mrr_at_1000 |
|
value: 87.601 |
|
- type: mrr_at_3 |
|
value: 86.503 |
|
- type: mrr_at_5 |
|
value: 87.179 |
|
- type: ndcg_at_1 |
|
value: 81.36 |
|
- type: ndcg_at_10 |
|
value: 88.319 |
|
- type: ndcg_at_100 |
|
value: 89.517 |
|
- type: ndcg_at_1000 |
|
value: 89.60900000000001 |
|
- type: ndcg_at_3 |
|
value: 85.423 |
|
- type: ndcg_at_5 |
|
value: 86.976 |
|
- type: precision_at_1 |
|
value: 81.36 |
|
- type: precision_at_10 |
|
value: 13.415 |
|
- type: precision_at_100 |
|
value: 1.529 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.342999999999996 |
|
- type: precision_at_5 |
|
value: 24.534 |
|
- type: recall_at_1 |
|
value: 70.62 |
|
- type: recall_at_10 |
|
value: 95.57600000000001 |
|
- type: recall_at_100 |
|
value: 99.624 |
|
- type: recall_at_1000 |
|
value: 99.991 |
|
- type: recall_at_3 |
|
value: 87.22 |
|
- type: recall_at_5 |
|
value: 91.654 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 60.826438478212744 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 64.24027467551447 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.997999999999999 |
|
- type: map_at_10 |
|
value: 14.267 |
|
- type: map_at_100 |
|
value: 16.843 |
|
- type: map_at_1000 |
|
value: 17.229 |
|
- type: map_at_3 |
|
value: 9.834 |
|
- type: map_at_5 |
|
value: 11.92 |
|
- type: mrr_at_1 |
|
value: 24.7 |
|
- type: mrr_at_10 |
|
value: 37.685 |
|
- type: mrr_at_100 |
|
value: 38.704 |
|
- type: mrr_at_1000 |
|
value: 38.747 |
|
- type: mrr_at_3 |
|
value: 34.150000000000006 |
|
- type: mrr_at_5 |
|
value: 36.075 |
|
- type: ndcg_at_1 |
|
value: 24.7 |
|
- type: ndcg_at_10 |
|
value: 23.44 |
|
- type: ndcg_at_100 |
|
value: 32.617000000000004 |
|
- type: ndcg_at_1000 |
|
value: 38.628 |
|
- type: ndcg_at_3 |
|
value: 21.747 |
|
- type: ndcg_at_5 |
|
value: 19.076 |
|
- type: precision_at_1 |
|
value: 24.7 |
|
- type: precision_at_10 |
|
value: 12.47 |
|
- type: precision_at_100 |
|
value: 2.564 |
|
- type: precision_at_1000 |
|
value: 0.4 |
|
- type: precision_at_3 |
|
value: 20.767 |
|
- type: precision_at_5 |
|
value: 17.06 |
|
- type: recall_at_1 |
|
value: 4.997999999999999 |
|
- type: recall_at_10 |
|
value: 25.3 |
|
- type: recall_at_100 |
|
value: 52.048 |
|
- type: recall_at_1000 |
|
value: 81.093 |
|
- type: recall_at_3 |
|
value: 12.642999999999999 |
|
- type: recall_at_5 |
|
value: 17.312 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.44942006292234 |
|
- type: cos_sim_spearman |
|
value: 79.80930790660699 |
|
- type: euclidean_pearson |
|
value: 82.93400777494863 |
|
- type: euclidean_spearman |
|
value: 80.04664991110705 |
|
- type: manhattan_pearson |
|
value: 82.93551681854949 |
|
- type: manhattan_spearman |
|
value: 80.03156736837379 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.63574059135726 |
|
- type: cos_sim_spearman |
|
value: 76.80552915288186 |
|
- type: euclidean_pearson |
|
value: 82.46368529820518 |
|
- type: euclidean_spearman |
|
value: 76.60338474719275 |
|
- type: manhattan_pearson |
|
value: 82.4558617035968 |
|
- type: manhattan_spearman |
|
value: 76.57936082895705 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.24116811084211 |
|
- type: cos_sim_spearman |
|
value: 88.10998662068769 |
|
- type: euclidean_pearson |
|
value: 87.04961732352689 |
|
- type: euclidean_spearman |
|
value: 88.12543945864087 |
|
- type: manhattan_pearson |
|
value: 86.9905224528854 |
|
- type: manhattan_spearman |
|
value: 88.07827944705546 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.74847296555048 |
|
- type: cos_sim_spearman |
|
value: 82.66200957916445 |
|
- type: euclidean_pearson |
|
value: 84.48132256004965 |
|
- type: euclidean_spearman |
|
value: 82.67915286000596 |
|
- type: manhattan_pearson |
|
value: 84.44950477268334 |
|
- type: manhattan_spearman |
|
value: 82.63327639173352 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 87.23056258027053 |
|
- type: cos_sim_spearman |
|
value: 88.92791680286955 |
|
- type: euclidean_pearson |
|
value: 88.13819235461933 |
|
- type: euclidean_spearman |
|
value: 88.87294661361716 |
|
- type: manhattan_pearson |
|
value: 88.14212133687899 |
|
- type: manhattan_spearman |
|
value: 88.88551854529777 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.64179522732887 |
|
- type: cos_sim_spearman |
|
value: 84.25028809903114 |
|
- type: euclidean_pearson |
|
value: 83.40175015236979 |
|
- type: euclidean_spearman |
|
value: 84.23369296429406 |
|
- type: manhattan_pearson |
|
value: 83.43768174261321 |
|
- type: manhattan_spearman |
|
value: 84.27855229214734 |
|
- 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: 88.20378955494732 |
|
- type: cos_sim_spearman |
|
value: 88.46863559173111 |
|
- type: euclidean_pearson |
|
value: 88.8249295811663 |
|
- type: euclidean_spearman |
|
value: 88.6312737724905 |
|
- type: manhattan_pearson |
|
value: 88.87744466378827 |
|
- type: manhattan_spearman |
|
value: 88.82908423767314 |
|
- 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: 69.91342028796086 |
|
- type: cos_sim_spearman |
|
value: 69.71495021867864 |
|
- type: euclidean_pearson |
|
value: 70.65334330405646 |
|
- type: euclidean_spearman |
|
value: 69.4321253472211 |
|
- type: manhattan_pearson |
|
value: 70.59743494727465 |
|
- type: manhattan_spearman |
|
value: 69.11695509297482 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.42451709766952 |
|
- type: cos_sim_spearman |
|
value: 86.07166710670508 |
|
- type: euclidean_pearson |
|
value: 86.12711421258899 |
|
- type: euclidean_spearman |
|
value: 86.05232086925126 |
|
- type: manhattan_pearson |
|
value: 86.15591089932126 |
|
- type: manhattan_spearman |
|
value: 86.0890128623439 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 87.1976344717285 |
|
- type: mrr |
|
value: 96.3703145075694 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 59.511 |
|
- type: map_at_10 |
|
value: 69.724 |
|
- type: map_at_100 |
|
value: 70.208 |
|
- type: map_at_1000 |
|
value: 70.22800000000001 |
|
- type: map_at_3 |
|
value: 66.986 |
|
- type: map_at_5 |
|
value: 68.529 |
|
- type: mrr_at_1 |
|
value: 62.333000000000006 |
|
- type: mrr_at_10 |
|
value: 70.55 |
|
- type: mrr_at_100 |
|
value: 70.985 |
|
- type: mrr_at_1000 |
|
value: 71.004 |
|
- type: mrr_at_3 |
|
value: 68.611 |
|
- type: mrr_at_5 |
|
value: 69.728 |
|
- type: ndcg_at_1 |
|
value: 62.333000000000006 |
|
- type: ndcg_at_10 |
|
value: 74.265 |
|
- type: ndcg_at_100 |
|
value: 76.361 |
|
- type: ndcg_at_1000 |
|
value: 76.82900000000001 |
|
- type: ndcg_at_3 |
|
value: 69.772 |
|
- type: ndcg_at_5 |
|
value: 71.94800000000001 |
|
- type: precision_at_1 |
|
value: 62.333000000000006 |
|
- type: precision_at_10 |
|
value: 9.9 |
|
- type: precision_at_100 |
|
value: 1.093 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 27.444000000000003 |
|
- type: precision_at_5 |
|
value: 18 |
|
- type: recall_at_1 |
|
value: 59.511 |
|
- type: recall_at_10 |
|
value: 87.156 |
|
- type: recall_at_100 |
|
value: 96.5 |
|
- type: recall_at_1000 |
|
value: 100 |
|
- type: recall_at_3 |
|
value: 75.2 |
|
- type: recall_at_5 |
|
value: 80.661 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.81683168316832 |
|
- type: cos_sim_ap |
|
value: 95.74716566563774 |
|
- type: cos_sim_f1 |
|
value: 90.64238745574103 |
|
- type: cos_sim_precision |
|
value: 91.7093142272262 |
|
- type: cos_sim_recall |
|
value: 89.60000000000001 |
|
- type: dot_accuracy |
|
value: 99.69405940594059 |
|
- type: dot_ap |
|
value: 91.09013507754594 |
|
- type: dot_f1 |
|
value: 84.54227113556779 |
|
- type: dot_precision |
|
value: 84.58458458458459 |
|
- type: dot_recall |
|
value: 84.5 |
|
- type: euclidean_accuracy |
|
value: 99.81782178217821 |
|
- type: euclidean_ap |
|
value: 95.6324301072609 |
|
- type: euclidean_f1 |
|
value: 90.58341862845445 |
|
- type: euclidean_precision |
|
value: 92.76729559748428 |
|
- type: euclidean_recall |
|
value: 88.5 |
|
- type: manhattan_accuracy |
|
value: 99.81980198019802 |
|
- type: manhattan_ap |
|
value: 95.68510494437183 |
|
- type: manhattan_f1 |
|
value: 90.58945191313342 |
|
- type: manhattan_precision |
|
value: 93.79014989293361 |
|
- type: manhattan_recall |
|
value: 87.6 |
|
- type: max_accuracy |
|
value: 99.81980198019802 |
|
- type: max_ap |
|
value: 95.74716566563774 |
|
- type: max_f1 |
|
value: 90.64238745574103 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 67.63761899427078 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 36.572473369697235 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 53.63000245208579 |
|
- type: mrr |
|
value: 54.504193722943725 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.300791939416545 |
|
- type: cos_sim_spearman |
|
value: 31.662904057924123 |
|
- type: dot_pearson |
|
value: 26.21198530758316 |
|
- type: dot_spearman |
|
value: 27.006921548904263 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.197 |
|
- type: map_at_10 |
|
value: 1.752 |
|
- type: map_at_100 |
|
value: 10.795 |
|
- type: map_at_1000 |
|
value: 27.18 |
|
- type: map_at_3 |
|
value: 0.5890000000000001 |
|
- type: map_at_5 |
|
value: 0.938 |
|
- type: mrr_at_1 |
|
value: 74 |
|
- type: mrr_at_10 |
|
value: 85.833 |
|
- type: mrr_at_100 |
|
value: 85.833 |
|
- type: mrr_at_1000 |
|
value: 85.833 |
|
- type: mrr_at_3 |
|
value: 85.333 |
|
- type: mrr_at_5 |
|
value: 85.833 |
|
- type: ndcg_at_1 |
|
value: 69 |
|
- type: ndcg_at_10 |
|
value: 70.22 |
|
- type: ndcg_at_100 |
|
value: 55.785 |
|
- type: ndcg_at_1000 |
|
value: 52.93600000000001 |
|
- type: ndcg_at_3 |
|
value: 72.084 |
|
- type: ndcg_at_5 |
|
value: 71.184 |
|
- type: precision_at_1 |
|
value: 74 |
|
- type: precision_at_10 |
|
value: 75.2 |
|
- type: precision_at_100 |
|
value: 57.3 |
|
- type: precision_at_1000 |
|
value: 23.302 |
|
- type: precision_at_3 |
|
value: 77.333 |
|
- type: precision_at_5 |
|
value: 75.6 |
|
- type: recall_at_1 |
|
value: 0.197 |
|
- type: recall_at_10 |
|
value: 2.019 |
|
- type: recall_at_100 |
|
value: 14.257 |
|
- type: recall_at_1000 |
|
value: 50.922 |
|
- type: recall_at_3 |
|
value: 0.642 |
|
- type: recall_at_5 |
|
value: 1.043 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.803 |
|
- type: map_at_10 |
|
value: 10.407 |
|
- type: map_at_100 |
|
value: 16.948 |
|
- type: map_at_1000 |
|
value: 18.424 |
|
- type: map_at_3 |
|
value: 5.405 |
|
- type: map_at_5 |
|
value: 6.908 |
|
- type: mrr_at_1 |
|
value: 36.735 |
|
- type: mrr_at_10 |
|
value: 50.221000000000004 |
|
- type: mrr_at_100 |
|
value: 51.388 |
|
- type: mrr_at_1000 |
|
value: 51.402 |
|
- type: mrr_at_3 |
|
value: 47.278999999999996 |
|
- type: mrr_at_5 |
|
value: 49.626 |
|
- type: ndcg_at_1 |
|
value: 34.694 |
|
- type: ndcg_at_10 |
|
value: 25.507 |
|
- type: ndcg_at_100 |
|
value: 38.296 |
|
- type: ndcg_at_1000 |
|
value: 49.492000000000004 |
|
- type: ndcg_at_3 |
|
value: 29.006999999999998 |
|
- type: ndcg_at_5 |
|
value: 25.979000000000003 |
|
- type: precision_at_1 |
|
value: 36.735 |
|
- type: precision_at_10 |
|
value: 22.041 |
|
- type: precision_at_100 |
|
value: 8.02 |
|
- type: precision_at_1000 |
|
value: 1.567 |
|
- type: precision_at_3 |
|
value: 28.571 |
|
- type: precision_at_5 |
|
value: 24.490000000000002 |
|
- type: recall_at_1 |
|
value: 2.803 |
|
- type: recall_at_10 |
|
value: 16.378 |
|
- type: recall_at_100 |
|
value: 50.489 |
|
- type: recall_at_1000 |
|
value: 85.013 |
|
- type: recall_at_3 |
|
value: 6.505 |
|
- type: recall_at_5 |
|
value: 9.243 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 70.55579999999999 |
|
- type: ap |
|
value: 14.206982753316227 |
|
- type: f1 |
|
value: 54.372142814964285 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 56.57611771363893 |
|
- type: f1 |
|
value: 56.924172639063144 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 52.82304915719759 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 85.92716218632653 |
|
- type: cos_sim_ap |
|
value: 73.73359122546046 |
|
- type: cos_sim_f1 |
|
value: 68.42559487116262 |
|
- type: cos_sim_precision |
|
value: 64.22124508215691 |
|
- type: cos_sim_recall |
|
value: 73.21899736147758 |
|
- type: dot_accuracy |
|
value: 80.38981939560112 |
|
- type: dot_ap |
|
value: 54.61060862444974 |
|
- type: dot_f1 |
|
value: 53.45710627400769 |
|
- type: dot_precision |
|
value: 44.87638839125761 |
|
- type: dot_recall |
|
value: 66.09498680738787 |
|
- type: euclidean_accuracy |
|
value: 86.02849138701794 |
|
- type: euclidean_ap |
|
value: 73.95673761922404 |
|
- type: euclidean_f1 |
|
value: 68.6783042394015 |
|
- type: euclidean_precision |
|
value: 65.1063829787234 |
|
- type: euclidean_recall |
|
value: 72.66490765171504 |
|
- type: manhattan_accuracy |
|
value: 85.9808070572808 |
|
- type: manhattan_ap |
|
value: 73.9050720058029 |
|
- type: manhattan_f1 |
|
value: 68.57560618983794 |
|
- type: manhattan_precision |
|
value: 63.70839936608558 |
|
- type: manhattan_recall |
|
value: 74.24802110817942 |
|
- type: max_accuracy |
|
value: 86.02849138701794 |
|
- type: max_ap |
|
value: 73.95673761922404 |
|
- type: max_f1 |
|
value: 68.6783042394015 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 88.72783017037295 |
|
- type: cos_sim_ap |
|
value: 85.52705223340233 |
|
- type: cos_sim_f1 |
|
value: 77.91659078492079 |
|
- type: cos_sim_precision |
|
value: 73.93378032764221 |
|
- type: cos_sim_recall |
|
value: 82.35294117647058 |
|
- type: dot_accuracy |
|
value: 85.41739434159972 |
|
- type: dot_ap |
|
value: 77.17734818118443 |
|
- type: dot_f1 |
|
value: 71.63473589973144 |
|
- type: dot_precision |
|
value: 66.96123719622415 |
|
- type: dot_recall |
|
value: 77.00954727440714 |
|
- type: euclidean_accuracy |
|
value: 88.68125897465751 |
|
- type: euclidean_ap |
|
value: 85.47712213906692 |
|
- type: euclidean_f1 |
|
value: 77.81419950830664 |
|
- type: euclidean_precision |
|
value: 75.37162649733006 |
|
- type: euclidean_recall |
|
value: 80.42038805050817 |
|
- type: manhattan_accuracy |
|
value: 88.67349710870494 |
|
- type: manhattan_ap |
|
value: 85.46506475241955 |
|
- type: manhattan_f1 |
|
value: 77.87259084890393 |
|
- type: manhattan_precision |
|
value: 74.54929577464789 |
|
- type: manhattan_recall |
|
value: 81.50600554357868 |
|
- type: max_accuracy |
|
value: 88.72783017037295 |
|
- type: max_ap |
|
value: 85.52705223340233 |
|
- type: max_f1 |
|
value: 77.91659078492079 |
|
language: |
|
- en |
|
license: mit |
|
--- |
|
|
|
# gte-large |
|
|
|
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) |
|
|
|
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. |
|
|
|
## Metrics |
|
|
|
We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). |
|
|
|
|
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | |
|
| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | |
|
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | |
|
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | |
|
| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | |
|
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | |
|
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | |
|
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | |
|
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | |
|
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | |
|
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | |
|
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | |
|
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | |
|
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | |
|
|
|
|
|
## Usage |
|
|
|
Code example |
|
|
|
```python |
|
import torch.nn.functional as F |
|
from torch import Tensor |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
def average_pool(last_hidden_states: Tensor, |
|
attention_mask: Tensor) -> Tensor: |
|
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
|
|
input_texts = [ |
|
"what is the capital of China?", |
|
"how to implement quick sort in python?", |
|
"Beijing", |
|
"sorting algorithms" |
|
] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large") |
|
model = AutoModel.from_pretrained("thenlper/gte-large") |
|
|
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
|
|
|
# (Optionally) normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:1] @ embeddings[1:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
Use with sentence-transformers: |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
sentences = ['That is a happy person', 'That is a very happy person'] |
|
|
|
model = SentenceTransformer('thenlper/gte-large') |
|
embeddings = model.encode(sentences) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
### Limitation |
|
|
|
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. |
|
|
|
### Citation |
|
|
|
If you find our paper or models helpful, please consider citing them as follows: |
|
|
|
``` |
|
@article{li2023towards, |
|
title={Towards general text embeddings with multi-stage contrastive learning}, |
|
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, |
|
journal={arXiv preprint arXiv:2308.03281}, |
|
year={2023} |
|
} |
|
``` |
