|
--- |
|
tags: |
|
- mteb |
|
model-index: |
|
- name: Solon-embeddings-large-0.1 |
|
results: |
|
- task: |
|
type: sentence-similarity |
|
name: Passage Retrieval |
|
dataset: |
|
type: unicamp-dl/mmarco |
|
name: mMARCO-fr |
|
config: french |
|
split: validation |
|
metrics: |
|
- type: recall_at_500 |
|
name: Recall@500 |
|
value: 92.7 |
|
- type: recall_at_100 |
|
name: Recall@100 |
|
value: 82.7 |
|
- type: recall_at_10 |
|
name: Recall@10 |
|
value: 55.5 |
|
- type: map_at_10 |
|
name: MAP@10 |
|
value: 29.4 |
|
- type: ndcg_at_10 |
|
name: nDCG@10 |
|
value: 35.8 |
|
- type: mrr_at_10 |
|
name: MRR@10 |
|
value: 29.9 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: lyon-nlp/alloprof |
|
name: MTEB AlloProfClusteringP2P |
|
config: default |
|
split: test |
|
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b |
|
metrics: |
|
- type: v_measure |
|
value: 64.16942168287153 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: lyon-nlp/alloprof |
|
name: MTEB AlloProfClusteringS2S |
|
config: default |
|
split: test |
|
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b |
|
metrics: |
|
- type: v_measure |
|
value: 38.17076313383054 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p |
|
name: MTEB AlloprofReranking |
|
config: default |
|
split: test |
|
revision: 666fdacebe0291776e86f29345663dfaf80a0db9 |
|
metrics: |
|
- type: map |
|
value: 64.8770878097632 |
|
- type: mrr |
|
value: 66.39132423169396 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: lyon-nlp/alloprof |
|
name: MTEB AlloprofRetrieval |
|
config: default |
|
split: test |
|
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.62 |
|
- type: map_at_10 |
|
value: 40.963 |
|
- type: map_at_100 |
|
value: 41.894 |
|
- type: map_at_1000 |
|
value: 41.939 |
|
- type: map_at_3 |
|
value: 37.708999999999996 |
|
- type: map_at_5 |
|
value: 39.696999999999996 |
|
- type: mrr_at_1 |
|
value: 29.62 |
|
- type: mrr_at_10 |
|
value: 40.963 |
|
- type: mrr_at_100 |
|
value: 41.894 |
|
- type: mrr_at_1000 |
|
value: 41.939 |
|
- type: mrr_at_3 |
|
value: 37.708999999999996 |
|
- type: mrr_at_5 |
|
value: 39.696999999999996 |
|
- type: ndcg_at_1 |
|
value: 29.62 |
|
- type: ndcg_at_10 |
|
value: 46.942 |
|
- type: ndcg_at_100 |
|
value: 51.629999999999995 |
|
- type: ndcg_at_1000 |
|
value: 52.927 |
|
- type: ndcg_at_3 |
|
value: 40.333999999999996 |
|
- type: ndcg_at_5 |
|
value: 43.922 |
|
- type: precision_at_1 |
|
value: 29.62 |
|
- type: precision_at_10 |
|
value: 6.589 |
|
- type: precision_at_100 |
|
value: 0.882 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 15.976 |
|
- type: precision_at_5 |
|
value: 11.33 |
|
- type: recall_at_1 |
|
value: 29.62 |
|
- type: recall_at_10 |
|
value: 65.889 |
|
- type: recall_at_100 |
|
value: 88.212 |
|
- type: recall_at_1000 |
|
value: 98.575 |
|
- type: recall_at_3 |
|
value: 47.927 |
|
- type: recall_at_5 |
|
value: 56.64900000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (fr) |
|
config: fr |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 42.077999999999996 |
|
- type: f1 |
|
value: 40.64511241732637 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: maastrichtlawtech/bsard |
|
name: MTEB BSARDRetrieval |
|
config: default |
|
split: test |
|
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.901 |
|
- type: map_at_10 |
|
value: 1.524 |
|
- type: map_at_100 |
|
value: 1.833 |
|
- type: map_at_1000 |
|
value: 1.916 |
|
- type: map_at_3 |
|
value: 1.276 |
|
- type: map_at_5 |
|
value: 1.276 |
|
- type: mrr_at_1 |
|
value: 0.901 |
|
- type: mrr_at_10 |
|
value: 1.524 |
|
- type: mrr_at_100 |
|
value: 1.833 |
|
- type: mrr_at_1000 |
|
value: 1.916 |
|
- type: mrr_at_3 |
|
value: 1.276 |
|
- type: mrr_at_5 |
|
value: 1.276 |
|
- type: ndcg_at_1 |
|
value: 0.901 |
|
- type: ndcg_at_10 |
|
value: 2.085 |
|
- type: ndcg_at_100 |
|
value: 3.805 |
|
- type: ndcg_at_1000 |
|
value: 6.704000000000001 |
|
- type: ndcg_at_3 |
|
value: 1.41 |
|
- type: ndcg_at_5 |
|
value: 1.41 |
|
- type: precision_at_1 |
|
value: 0.901 |
|
- type: precision_at_10 |
|
value: 0.40499999999999997 |
|
- type: precision_at_100 |
|
value: 0.126 |
|
- type: precision_at_1000 |
|
value: 0.037 |
|
- type: precision_at_3 |
|
value: 0.601 |
|
- type: precision_at_5 |
|
value: 0.36 |
|
- type: recall_at_1 |
|
value: 0.901 |
|
- type: recall_at_10 |
|
value: 4.054 |
|
- type: recall_at_100 |
|
value: 12.613 |
|
- type: recall_at_1000 |
|
value: 36.937 |
|
- type: recall_at_3 |
|
value: 1.802 |
|
- type: recall_at_5 |
|
value: 1.802 |
|
- task: |
|
type: BitextMining |
|
dataset: |
|
type: rbawden/DiaBLa |
|
name: MTEB DiaBLaBitextMining (fr-en) |
|
config: fr-en |
|
split: test |
|
revision: 5345895c56a601afe1a98519ce3199be60a27dba |
|
metrics: |
|
- type: accuracy |
|
value: 88.90048712595686 |
|
- type: f1 |
|
value: 86.94952864886115 |
|
- type: precision |
|
value: 86.20344379175826 |
|
- type: recall |
|
value: 88.90048712595686 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: lyon-nlp/clustering-hal-s2s |
|
name: MTEB HALClusteringS2S |
|
config: default |
|
split: test |
|
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 |
|
metrics: |
|
- type: v_measure |
|
value: 24.087988843991155 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mlsum |
|
name: MTEB MLSUMClusteringP2P |
|
config: default |
|
split: test |
|
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
|
metrics: |
|
- type: v_measure |
|
value: 43.79603865728535 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mlsum |
|
name: MTEB MLSUMClusteringS2S |
|
config: default |
|
split: test |
|
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 |
|
metrics: |
|
- type: v_measure |
|
value: 37.746550373003 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (fr) |
|
config: fr |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 89.26088318196052 |
|
- type: f1 |
|
value: 88.95811185929033 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (fr) |
|
config: fr |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 68.55308487316003 |
|
- type: f1 |
|
value: 48.2936682439785 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClassification (fra) |
|
config: fra |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: accuracy |
|
value: 81.51658767772511 |
|
- type: f1 |
|
value: 77.695234448912 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringP2P (fra) |
|
config: fra |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 40.80377094681114 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: masakhane/masakhanews |
|
name: MTEB MasakhaNEWSClusteringS2S (fra) |
|
config: fra |
|
split: test |
|
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
|
metrics: |
|
- type: v_measure |
|
value: 28.79703837416241 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (fr) |
|
config: fr |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 67.40080699394755 |
|
- type: f1 |
|
value: 65.60793135686376 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (fr) |
|
config: fr |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 71.29455279085406 |
|
- type: f1 |
|
value: 70.80876673828983 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: jinaai/mintakaqa |
|
name: MTEB MintakaRetrieval (fr) |
|
config: fr |
|
split: test |
|
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.625999999999998 |
|
- type: map_at_10 |
|
value: 25.224999999999998 |
|
- type: map_at_100 |
|
value: 26.291999999999998 |
|
- type: map_at_1000 |
|
value: 26.395000000000003 |
|
- type: map_at_3 |
|
value: 22.378999999999998 |
|
- type: map_at_5 |
|
value: 24.009 |
|
- type: mrr_at_1 |
|
value: 16.625999999999998 |
|
- type: mrr_at_10 |
|
value: 25.224999999999998 |
|
- type: mrr_at_100 |
|
value: 26.291999999999998 |
|
- type: mrr_at_1000 |
|
value: 26.395000000000003 |
|
- type: mrr_at_3 |
|
value: 22.378999999999998 |
|
- type: mrr_at_5 |
|
value: 24.009 |
|
- type: ndcg_at_1 |
|
value: 16.625999999999998 |
|
- type: ndcg_at_10 |
|
value: 30.074 |
|
- type: ndcg_at_100 |
|
value: 35.683 |
|
- type: ndcg_at_1000 |
|
value: 38.714999999999996 |
|
- type: ndcg_at_3 |
|
value: 24.188000000000002 |
|
- type: ndcg_at_5 |
|
value: 27.124 |
|
- type: precision_at_1 |
|
value: 16.625999999999998 |
|
- type: precision_at_10 |
|
value: 4.566 |
|
- type: precision_at_100 |
|
value: 0.729 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 9.801 |
|
- type: precision_at_5 |
|
value: 7.305000000000001 |
|
- type: recall_at_1 |
|
value: 16.625999999999998 |
|
- type: recall_at_10 |
|
value: 45.659 |
|
- type: recall_at_100 |
|
value: 72.85000000000001 |
|
- type: recall_at_1000 |
|
value: 97.42 |
|
- type: recall_at_3 |
|
value: 29.402 |
|
- type: recall_at_5 |
|
value: 36.527 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: GEM/opusparcus |
|
name: MTEB OpusparcusPC (fr) |
|
config: fr |
|
split: test |
|
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 83.58310626702998 |
|
- type: cos_sim_ap |
|
value: 94.01979957812989 |
|
- type: cos_sim_f1 |
|
value: 88.70135958743555 |
|
- type: cos_sim_precision |
|
value: 84.01420959147424 |
|
- type: cos_sim_recall |
|
value: 93.94240317775571 |
|
- type: dot_accuracy |
|
value: 83.58310626702998 |
|
- type: dot_ap |
|
value: 94.01979957812989 |
|
- type: dot_f1 |
|
value: 88.70135958743555 |
|
- type: dot_precision |
|
value: 84.01420959147424 |
|
- type: dot_recall |
|
value: 93.94240317775571 |
|
- type: euclidean_accuracy |
|
value: 83.58310626702998 |
|
- type: euclidean_ap |
|
value: 94.01979957812989 |
|
- type: euclidean_f1 |
|
value: 88.70135958743555 |
|
- type: euclidean_precision |
|
value: 84.01420959147424 |
|
- type: euclidean_recall |
|
value: 93.94240317775571 |
|
- type: manhattan_accuracy |
|
value: 83.58310626702998 |
|
- type: manhattan_ap |
|
value: 93.99936024003892 |
|
- type: manhattan_f1 |
|
value: 88.6924150767799 |
|
- type: manhattan_precision |
|
value: 83.45008756567425 |
|
- type: manhattan_recall |
|
value: 94.63753723932473 |
|
- type: max_accuracy |
|
value: 83.58310626702998 |
|
- type: max_ap |
|
value: 94.01979957812989 |
|
- type: max_f1 |
|
value: 88.70135958743555 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: paws-x |
|
name: MTEB PawsX (fr) |
|
config: fr |
|
split: test |
|
revision: 8a04d940a42cd40658986fdd8e3da561533a3646 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 60.6 |
|
- type: cos_sim_ap |
|
value: 60.18915797975459 |
|
- type: cos_sim_f1 |
|
value: 62.491349480968864 |
|
- type: cos_sim_precision |
|
value: 45.44539506794162 |
|
- type: cos_sim_recall |
|
value: 100 |
|
- type: dot_accuracy |
|
value: 60.6 |
|
- type: dot_ap |
|
value: 60.091135216056024 |
|
- type: dot_f1 |
|
value: 62.491349480968864 |
|
- type: dot_precision |
|
value: 45.44539506794162 |
|
- type: dot_recall |
|
value: 100 |
|
- type: euclidean_accuracy |
|
value: 60.6 |
|
- type: euclidean_ap |
|
value: 60.18915797975459 |
|
- type: euclidean_f1 |
|
value: 62.491349480968864 |
|
- type: euclidean_precision |
|
value: 45.44539506794162 |
|
- type: euclidean_recall |
|
value: 100 |
|
- type: manhattan_accuracy |
|
value: 60.650000000000006 |
|
- type: manhattan_ap |
|
value: 60.2082343915352 |
|
- type: manhattan_f1 |
|
value: 62.491349480968864 |
|
- type: manhattan_precision |
|
value: 45.44539506794162 |
|
- type: manhattan_recall |
|
value: 100 |
|
- type: max_accuracy |
|
value: 60.650000000000006 |
|
- type: max_ap |
|
value: 60.2082343915352 |
|
- type: max_f1 |
|
value: 62.491349480968864 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: Lajavaness/SICK-fr |
|
name: MTEB SICKFr |
|
config: default |
|
split: test |
|
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.77067200230256 |
|
- type: cos_sim_spearman |
|
value: 76.7445532523278 |
|
- type: euclidean_pearson |
|
value: 76.34017074673956 |
|
- type: euclidean_spearman |
|
value: 76.7453011027832 |
|
- type: manhattan_pearson |
|
value: 76.19578084197778 |
|
- type: manhattan_spearman |
|
value: 76.56293456459228 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (fr) |
|
config: fr |
|
split: test |
|
revision: eea2b4fe26a775864c896887d910b76a8098ad3f |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.2564160237984 |
|
- type: cos_sim_spearman |
|
value: 83.30552085410882 |
|
- type: euclidean_pearson |
|
value: 82.00494560507786 |
|
- type: euclidean_spearman |
|
value: 83.30552085410882 |
|
- type: manhattan_pearson |
|
value: 81.93132229157803 |
|
- type: manhattan_spearman |
|
value: 83.04357992939353 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: stsb_multi_mt |
|
name: MTEB STSBenchmarkMultilingualSTS (fr) |
|
config: fr |
|
split: test |
|
revision: 93d57ef91790589e3ce9c365164337a8a78b7632 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.34931905288978 |
|
- type: cos_sim_spearman |
|
value: 79.99372771100049 |
|
- type: euclidean_pearson |
|
value: 78.37976845123443 |
|
- type: euclidean_spearman |
|
value: 79.99452356550658 |
|
- type: manhattan_pearson |
|
value: 78.24434042082316 |
|
- type: manhattan_spearman |
|
value: 79.87248340061164 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: lyon-nlp/summarization-summeval-fr-p2p |
|
name: MTEB SummEvalFr |
|
config: default |
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split: test |
|
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 30.476001473421586 |
|
- type: cos_sim_spearman |
|
value: 29.687350195905456 |
|
- type: dot_pearson |
|
value: 30.476000875190685 |
|
- type: dot_spearman |
|
value: 29.662224660056562 |
|
- task: |
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type: Reranking |
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dataset: |
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type: lyon-nlp/mteb-fr-reranking-syntec-s2p |
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name: MTEB SyntecReranking |
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config: default |
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split: test |
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revision: b205c5084a0934ce8af14338bf03feb19499c84d |
|
metrics: |
|
- type: map |
|
value: 88.28333333333333 |
|
- type: mrr |
|
value: 88.28333333333333 |
|
- task: |
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type: Retrieval |
|
dataset: |
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type: lyon-nlp/mteb-fr-retrieval-syntec-s2p |
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name: MTEB SyntecRetrieval |
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config: default |
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split: test |
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revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff |
|
metrics: |
|
- type: map_at_1 |
|
value: 69 |
|
- type: map_at_10 |
|
value: 79.906 |
|
- type: map_at_100 |
|
value: 79.982 |
|
- type: map_at_1000 |
|
value: 79.982 |
|
- type: map_at_3 |
|
value: 77.667 |
|
- type: map_at_5 |
|
value: 79.51700000000001 |
|
- type: mrr_at_1 |
|
value: 69 |
|
- type: mrr_at_10 |
|
value: 79.906 |
|
- type: mrr_at_100 |
|
value: 79.982 |
|
- type: mrr_at_1000 |
|
value: 79.982 |
|
- type: mrr_at_3 |
|
value: 77.667 |
|
- type: mrr_at_5 |
|
value: 79.51700000000001 |
|
- type: ndcg_at_1 |
|
value: 69 |
|
- type: ndcg_at_10 |
|
value: 84.60499999999999 |
|
- type: ndcg_at_100 |
|
value: 84.868 |
|
- type: ndcg_at_1000 |
|
value: 84.868 |
|
- type: ndcg_at_3 |
|
value: 80.333 |
|
- type: ndcg_at_5 |
|
value: 83.647 |
|
- type: precision_at_1 |
|
value: 69 |
|
- type: precision_at_10 |
|
value: 9.9 |
|
- type: precision_at_100 |
|
value: 1 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 29.333 |
|
- type: precision_at_5 |
|
value: 19.2 |
|
- type: recall_at_1 |
|
value: 69 |
|
- type: recall_at_10 |
|
value: 99 |
|
- type: recall_at_100 |
|
value: 100 |
|
- type: recall_at_1000 |
|
value: 100 |
|
- type: recall_at_3 |
|
value: 88 |
|
- type: recall_at_5 |
|
value: 96 |
|
- task: |
|
type: Retrieval |
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dataset: |
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type: jinaai/xpqa |
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name: MTEB XPQARetrieval (fr) |
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config: fr |
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split: test |
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revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f |
|
metrics: |
|
- type: map_at_1 |
|
value: 42.027 |
|
- type: map_at_10 |
|
value: 64.331 |
|
- type: map_at_100 |
|
value: 65.657 |
|
- type: map_at_1000 |
|
value: 65.7 |
|
- type: map_at_3 |
|
value: 57.967999999999996 |
|
- type: map_at_5 |
|
value: 62.33800000000001 |
|
- type: mrr_at_1 |
|
value: 65.688 |
|
- type: mrr_at_10 |
|
value: 72.263 |
|
- type: mrr_at_100 |
|
value: 72.679 |
|
- type: mrr_at_1000 |
|
value: 72.69099999999999 |
|
- type: mrr_at_3 |
|
value: 70.405 |
|
- type: mrr_at_5 |
|
value: 71.587 |
|
- type: ndcg_at_1 |
|
value: 65.688 |
|
- type: ndcg_at_10 |
|
value: 70.221 |
|
- type: ndcg_at_100 |
|
value: 74.457 |
|
- type: ndcg_at_1000 |
|
value: 75.178 |
|
- type: ndcg_at_3 |
|
value: 65.423 |
|
- type: ndcg_at_5 |
|
value: 67.05499999999999 |
|
- type: precision_at_1 |
|
value: 65.688 |
|
- type: precision_at_10 |
|
value: 16.208 |
|
- type: precision_at_100 |
|
value: 1.975 |
|
- type: precision_at_1000 |
|
value: 0.207 |
|
- type: precision_at_3 |
|
value: 39.831 |
|
- type: precision_at_5 |
|
value: 28.652 |
|
- type: recall_at_1 |
|
value: 42.027 |
|
- type: recall_at_10 |
|
value: 78.803 |
|
- type: recall_at_100 |
|
value: 95.051 |
|
- type: recall_at_1000 |
|
value: 99.75500000000001 |
|
- type: recall_at_3 |
|
value: 62.62799999999999 |
|
- type: recall_at_5 |
|
value: 70.975 |
|
license: mit |
|
language: |
|
- fr |
|
--- |
|
|
|
# Solon Embeddings — large 0.1 |
|
|
|
SOTA Open source french embedding model. |
|
|
|
**Instructions :** |
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Add "query : " before the *query* to retrieve to increase performance of retrieval. |
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No instructions needed for *passages*. |
|
|
|
|
|
| Model | Mean Score | |
|
| --- | --- | |
|
| **OrdalieTech/Solon-embeddings-large-0.1** | 0.7490 | |
|
| cohere/embed-multilingual-v3 | 0.7402 | |
|
| **OrdalieTech/Solon-embeddings-base-0.1** | 0.7306 | |
|
| openai/ada-002 | 0.7290 | |
|
| cohere/embed-multilingual-light-v3 | 0.6945 | |
|
| antoinelouis/biencoder-camembert-base-mmarcoFR | 0.6826 | |
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| dangvantuan/sentence-camembert-large | 0.6756 | |
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| voyage/voyage-01 | 0.6753 | |
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| intfloat/multilingual-e5-large | 0.6660 | |
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| intfloat/multilingual-e5-base | 0.6597 | |
|
| Sbert/paraphrase-multilingual-mpnet-base-v2 | 0.5975 | |
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| dangvantuan/sentence-camembert-base | 0.5456 | |
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| EuropeanParliament/eubert_embedding_v1 | 0.5063 | |
|
|
|
These results have been obtained through 9 french benchmarks on a variety of text similarity tasks (classification, reranking, STS) : |
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- AmazonReviewsClassification (MTEB) |
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- MassiveIntentClassification (MTEB) |
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- MassiveScenarioClassification (MTEB) |
|
- MTOPDomainClassification (MTEB) |
|
- MTOPIntentClassification (MTEB) |
|
- STS22 (MTEB) |
|
- MiraclFRRerank (Miracl) |
|
- OrdalieFRSTS (Ordalie) |
|
- OrdalieFRReranking (Ordalie) |
|
|
|
We created OrdalieFRSTS and OrdalieFRReranking to enhance the benchmarking capabilities of French STS and reranking assessments. |
|
|
|
(evaluation script available here : github.com/OrdalieTech/mteb) |
