--- tags: - mteb model-index: - name: Solon-embeddings-large-0.1 results: - 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: 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 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: type: Reranking dataset: type: lyon-nlp/mteb-fr-reranking-syntec-s2p name: MTEB SyntecReranking config: default split: test revision: b205c5084a0934ce8af14338bf03feb19499c84d metrics: - type: map value: 88.28333333333333 - type: mrr value: 88.28333333333333 - task: type: Retrieval dataset: type: lyon-nlp/mteb-fr-retrieval-syntec-s2p name: MTEB SyntecRetrieval config: default split: test 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 dataset: type: jinaai/xpqa name: MTEB XPQARetrieval (fr) config: fr split: test 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 :** Add "query : " before the *query* to retrieve to increase performance of retrieval. 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 | | dangvantuan/sentence-camembert-large | 0.6756 | | voyage/voyage-01 | 0.6753 | | intfloat/multilingual-e5-large | 0.6660 | | intfloat/multilingual-e5-base | 0.6597 | | Sbert/paraphrase-multilingual-mpnet-base-v2 | 0.5975 | | dangvantuan/sentence-camembert-base | 0.5456 | | 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) : - AmazonReviewsClassification (MTEB) - MassiveIntentClassification (MTEB) - 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)