dinab/multilingual-e5-base-q4_k_m-gguf Q4_K_M GGUF - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.
Model Intelligence Sheet
dinab/multilingual-e5-base-q4_k_m-gguf overview
This model was converted to GGUF format from intfloat/multilingual-e5-base using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
Downloads
414
Likes
0
Pipeline
sentence-similarity
Library
sentence-transformers
Visibility
Public
Access
Open
Repository Files & Downloads
1 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| multilingual-e5-base-q4_k_m.gguf | GGUF | Q4_K_M | 208.60 MB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
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],
"frontmatter": {
"tags": [
"mteb",
"Sentence Transformers",
"sentence-similarity",
"sentence-transformers",
"llama-cpp",
"gguf-my-repo"
],
"language": [
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"'no'",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh"
],
"license": "mit",
"base_model": [
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"summary": "This model was converted to GGUF format from intfloat/multilingual-e5-base using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.",
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"readme_markdown": "---\ntags:\n- mteb\n- Sentence Transformers\n- sentence-similarity\n- sentence-transformers\n- llama-cpp\n- gguf-my-repo\nlanguage:\n- multilingual\n- af\n- am\n- ar\n- as\n- az\n- be\n- bg\n- bn\n- br\n- bs\n- ca\n- cs\n- cy\n- da\n- de\n- el\n- en\n- eo\n- es\n- et\n- eu\n- fa\n- fi\n- fr\n- fy\n- ga\n- gd\n- gl\n- gu\n- ha\n- he\n- hi\n- hr\n- hu\n- hy\n- id\n- is\n- it\n- ja\n- jv\n- ka\n- kk\n- km\n- kn\n- ko\n- ku\n- ky\n- la\n- lo\n- lt\n- lv\n- mg\n- mk\n- ml\n- mn\n- mr\n- ms\n- my\n- ne\n- nl\n- 'no'\n- om\n- or\n- pa\n- pl\n- ps\n- pt\n- ro\n- ru\n- sa\n- sd\n- si\n- sk\n- sl\n- so\n- sq\n- sr\n- su\n- sv\n- sw\n- ta\n- te\n- th\n- tl\n- tr\n- ug\n- uk\n- ur\n- uz\n- vi\n- xh\n- yi\n- zh\nlicense: mit\nbase_model: intfloat/multilingual-e5-base\nmodel-index:\n- name: multilingual-e5-base\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 78.97014925373135\n - type: ap\n value: 43.69351129103008\n - type: f1\n value: 73.38075030070492\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (de)\n type: mteb/amazon_counterfactual\n config: de\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 71.7237687366167\n - type: ap\n value: 82.22089859962671\n - type: f1\n value: 69.95532758884401\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en-ext)\n type: mteb/amazon_counterfactual\n config: en-ext\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 79.65517241379312\n - type: ap\n value: 28.507918657094738\n - type: f1\n value: 66.84516013726119\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (ja)\n type: mteb/amazon_counterfactual\n config: ja\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 73.32976445396146\n - type: ap\n value: 20.720481637566014\n - type: f1\n value: 59.78002763416003\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 90.63775\n - type: ap\n value: 87.22277903861716\n - type: f1\n value: 90.60378636386807\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 44.546\n - type: f1\n value: 44.05666638370923\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (de)\n type: mteb/amazon_reviews_multi\n config: de\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 41.828\n - type: f1\n value: 41.2710255644252\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (es)\n type: mteb/amazon_reviews_multi\n config: es\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 40.534\n - type: f1\n value: 39.820743174270326\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (fr)\n type: mteb/amazon_reviews_multi\n config: fr\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 39.684\n - type: f1\n value: 39.11052682815307\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (ja)\n type: mteb/amazon_reviews_multi\n config: ja\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 37.436\n - type: f1\n value: 37.07082931930871\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (zh)\n type: mteb/amazon_reviews_multi\n config: zh\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 37.226000000000006\n - type: f1\n value: 36.65372077739185\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 22.831000000000003\n - type: map_at_10\n value: 36.42\n - type: map_at_100\n value: 37.699\n - type: map_at_1000\n value: 37.724000000000004\n - type: map_at_3\n value: 32.207\n - type: map_at_5\n value: 34.312\n - type: mrr_at_1\n value: 23.257\n - type: mrr_at_10\n value: 36.574\n - type: mrr_at_100\n value: 37.854\n - type: mrr_at_1000\n value: 37.878\n - type: mrr_at_3\n value: 32.385000000000005\n - type: mrr_at_5\n value: 34.48\n - type: ndcg_at_1\n value: 22.831000000000003\n - type: ndcg_at_10\n value: 44.230000000000004\n - type: ndcg_at_100\n value: 49.974000000000004\n - type: ndcg_at_1000\n value: 50.522999999999996\n - type: ndcg_at_3\n value: 35.363\n - type: ndcg_at_5\n value: 39.164\n - type: precision_at_1\n value: 22.831000000000003\n - type: precision_at_10\n value: 6.935\n - type: precision_at_100\n value: 0.9520000000000001\n - type: precision_at_1000\n value: 0.099\n - type: precision_at_3\n value: 14.841\n - type: precision_at_5\n value: 10.754\n - type: recall_at_1\n value: 22.831000000000003\n - type: recall_at_10\n value: 69.346\n - type: recall_at_100\n value: 95.235\n - type: recall_at_1000\n value: 99.36\n - type: recall_at_3\n value: 44.523\n - type: recall_at_5\n value: 53.769999999999996\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 40.27789869854063\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 35.41979463347428\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 58.22752045109304\n - type: mrr\n value: 71.51112430198303\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 84.71147646622866\n - type: cos_sim_spearman\n value: 85.059167046486\n - type: euclidean_pearson\n value: 75.88421613600647\n - type: euclidean_spearman\n value: 75.12821787150585\n - type: manhattan_pearson\n value: 75.22005646957604\n - type: manhattan_spearman\n value: 74.42880434453272\n - task:\n type: BitextMining\n dataset:\n name: MTEB BUCC (de-en)\n type: mteb/bucc-bitext-mining\n config: de-en\n split: test\n revision: d51519689f32196a32af33b075a01d0e7c51e252\n metrics:\n - type: accuracy\n value: 99.23799582463465\n - type: f1\n value: 99.12665274878218\n - type: precision\n value: 99.07098121085595\n - type: recall\n value: 99.23799582463465\n - task:\n type: BitextMining\n dataset:\n name: MTEB BUCC (fr-en)\n type: mteb/bucc-bitext-mining\n config: fr-en\n split: test\n revision: d51519689f32196a32af33b075a01d0e7c51e252\n metrics:\n - type: accuracy\n value: 97.88685890380806\n - type: f1\n value: 97.59336708489249\n - type: precision\n value: 97.44662117543473\n - type: recall\n value: 97.88685890380806\n - task:\n type: BitextMining\n dataset:\n name: MTEB BUCC (ru-en)\n type: mteb/bucc-bitext-mining\n config: ru-en\n split: test\n revision: d51519689f32196a32af33b075a01d0e7c51e252\n metrics:\n - type: accuracy\n value: 97.47142362313821\n - type: f1\n value: 97.1989377670015\n - type: precision\n value: 97.06384944001847\n - type: recall\n value: 97.47142362313821\n - task:\n type: BitextMining\n dataset:\n name: MTEB BUCC (zh-en)\n type: mteb/bucc-bitext-mining\n config: zh-en\n split: test\n revision: d51519689f32196a32af33b075a01d0e7c51e252\n metrics:\n - type: accuracy\n value: 98.4728804634018\n - type: f1\n value: 98.2973494821836\n - type: precision\n value: 98.2095839915745\n - type: recall\n value: 98.4728804634018\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 82.74025974025975\n - type: f1\n value: 82.67420447730439\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 35.0380848063507\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 29.45956405670166\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 32.122\n - type: map_at_10\n value: 42.03\n - type: map_at_100\n value: 43.364000000000004\n - type: map_at_1000\n value: 43.474000000000004\n - type: map_at_3\n value: 38.804\n - type: map_at_5\n value: 40.585\n - type: mrr_at_1\n value: 39.914\n - type: mrr_at_10\n value: 48.227\n - type: mrr_at_100\n value: 49.018\n - type: mrr_at_1000\n value: 49.064\n - type: mrr_at_3\n value: 45.994\n - type: mrr_at_5\n value: 47.396\n - type: ndcg_at_1\n value: 39.914\n - type: ndcg_at_10\n value: 47.825\n - type: ndcg_at_100\n value: 52.852\n - type: ndcg_at_1000\n value: 54.891\n - type: ndcg_at_3\n value: 43.517\n - type: ndcg_at_5\n value: 45.493\n - type: precision_at_1\n value: 39.914\n - type: precision_at_10\n value: 8.956\n - type: precision_at_100\n value: 1.388\n - type: precision_at_1000\n value: 0.182\n - type: precision_at_3\n value: 20.791999999999998\n - type: precision_at_5\n value: 14.821000000000002\n - type: recall_at_1\n value: 32.122\n - type: recall_at_10\n value: 58.294999999999995\n - type: recall_at_100\n value: 79.726\n - type: recall_at_1000\n value: 93.099\n - type: recall_at_3\n value: 45.017\n - type: recall_at_5\n value: 51.002\n - type: map_at_1\n value: 29.677999999999997\n - type: map_at_10\n value: 38.684000000000005\n - type: map_at_100\n value: 39.812999999999995\n - type: map_at_1000\n value: 39.945\n - type: map_at_3\n value: 35.831\n - type: map_at_5\n value: 37.446\n - type: mrr_at_1\n value: 37.771\n - type: mrr_at_10\n value: 44.936\n - type: mrr_at_100\n value: 45.583\n - type: mrr_at_1000\n value: 45.634\n - type: mrr_at_3\n value: 42.771\n - type: mrr_at_5\n value: 43.994\n - type: ndcg_at_1\n value: 37.771\n - type: ndcg_at_10\n value: 44.059\n - type: ndcg_at_100\n value: 48.192\n - type: ndcg_at_1000\n value: 50.375\n - type: ndcg_at_3\n value: 40.172000000000004\n - type: ndcg_at_5\n value: 41.899\n - type: precision_at_1\n value: 37.771\n - type: precision_at_10\n value: 8.286999999999999\n - type: precision_at_100\n value: 1.322\n - type: precision_at_1000\n value: 0.178\n - type: precision_at_3\n value: 19.406000000000002\n - type: precision_at_5\n value: 13.745\n - type: recall_at_1\n value: 29.677999999999997\n - type: recall_at_10\n value: 53.071\n - type: recall_at_100\n value: 70.812\n - type: recall_at_1000\n value: 84.841\n - type: recall_at_3\n value: 41.016000000000005\n - type: recall_at_5\n value: 46.22\n - type: map_at_1\n value: 42.675000000000004\n - type: map_at_10\n value: 53.93599999999999\n - type: map_at_100\n value: 54.806999999999995\n - type: map_at_1000\n value: 54.867\n - type: map_at_3\n value: 50.934000000000005\n - type: map_at_5\n value: 52.583\n - type: mrr_at_1\n value: 48.339\n - type: mrr_at_10\n value: 57.265\n - type: mrr_at_100\n value: 57.873\n - type: mrr_at_1000\n value: 57.906\n - type: mrr_at_3\n value: 55.193000000000005\n - type: mrr_at_5\n value: 56.303000000000004\n - type: ndcg_at_1\n value: 48.339\n - type: ndcg_at_10\n value: 59.19799999999999\n - type: ndcg_at_100\n value: 62.743\n - type: ndcg_at_1000\n value: 63.99399999999999\n - type: ndcg_at_3\n value: 54.367\n - type: ndcg_at_5\n value: 56.548\n - type: precision_at_1\n value: 48.339\n - type: precision_at_10\n value: 9.216000000000001\n - type: precision_at_100\n value: 1.1809999999999998\n - type: precision_at_1000\n value: 0.134\n - type: precision_at_3\n value: 23.72\n - type: precision_at_5\n value: 16.025\n - type: recall_at_1\n value: 42.675000000000004\n - type: recall_at_10\n value: 71.437\n - type: recall_at_100\n value: 86.803\n - type: recall_at_1000\n value: 95.581\n - type: recall_at_3\n value: 58.434\n - type: recall_at_5\n value: 63.754\n - type: map_at_1\n value: 23.518\n - type: map_at_10\n value: 30.648999999999997\n - type: map_at_100\n value: 31.508999999999997\n - type: map_at_1000\n value: 31.604\n - type: map_at_3\n value: 28.247\n - type: map_at_5\n value: 29.65\n - type: mrr_at_1\n value: 25.650000000000002\n - type: mrr_at_10\n value: 32.771\n - type: mrr_at_100\n value: 33.554\n - type: mrr_at_1000\n value: 33.629999999999995\n - type: mrr_at_3\n value: 30.433\n - type: mrr_at_5\n value: 31.812\n - type: ndcg_at_1\n value: 25.650000000000002\n - type: ndcg_at_10\n value: 34.929\n - type: ndcg_at_100\n value: 39.382\n - type: ndcg_at_1000\n value: 41.913\n - type: ndcg_at_3\n value: 30.292\n - type: ndcg_at_5\n value: 32.629999999999995\n - type: precision_at_1\n value: 25.650000000000002\n - type: precision_at_10\n value: 5.311\n - type: precision_at_100\n value: 0.792\n - type: precision_at_1000\n value: 0.105\n - type: precision_at_3\n value: 12.58\n - type: precision_at_5\n value: 8.994\n - type: recall_at_1\n value: 23.518\n - type: recall_at_10\n value: 46.19\n - type: recall_at_100\n value: 67.123\n - type: recall_at_1000\n value: 86.442\n - type: recall_at_3\n value: 33.678000000000004\n - type: recall_at_5\n value: 39.244\n - type: map_at_1\n value: 15.891\n - type: map_at_10\n value: 22.464000000000002\n - type: map_at_100\n value: 23.483\n - type: map_at_1000\n value: 23.613\n - type: map_at_3\n value: 20.080000000000002\n - type: map_at_5\n value: 21.526\n - type: mrr_at_1\n value: 20.025000000000002\n - type: mrr_at_10\n value: 26.712999999999997\n - type: mrr_at_100\n value: 27.650000000000002\n - type: mrr_at_1000\n value: 27.737000000000002\n - type: mrr_at_3\n value: 24.274\n - type: mrr_at_5\n value: 25.711000000000002\n - type: ndcg_at_1\n value: 20.025000000000002\n - type: ndcg_at_10\n value: 27.028999999999996\n - type: ndcg_at_100\n value: 32.064\n - type: ndcg_at_1000\n value: 35.188\n - type: ndcg_at_3\n value: 22.512999999999998\n - type: ndcg_at_5\n value: 24.89\n - type: precision_at_1\n value: 20.025000000000002\n - type: precision_at_10\n value: 4.776\n - type: precision_at_100\n value: 0.8500000000000001\n - type: precision_at_1000\n value: 0.125\n - type: precision_at_3\n value: 10.531\n - type: precision_at_5\n value: 7.811\n - type: recall_at_1\n value: 15.891\n - type: recall_at_10\n value: 37.261\n - type: recall_at_100\n value: 59.12\n - type: recall_at_1000\n value: 81.356\n - type: recall_at_3\n value: 24.741\n - type: recall_at_5\n value: 30.753999999999998\n - type: map_at_1\n value: 27.544\n - type: map_at_10\n value: 36.283\n - type: map_at_100\n value: 37.467\n - type: map_at_1000\n value: 37.574000000000005\n - type: map_at_3\n value: 33.528999999999996\n - type: map_at_5\n value: 35.028999999999996\n - type: mrr_at_1\n value: 34.166999999999994\n - type: mrr_at_10\n value: 41.866\n - type: mrr_at_100\n value: 42.666\n - type: mrr_at_1000\n value: 42.716\n - type: mrr_at_3\n value: 39.541\n - type: mrr_at_5\n value: 40.768\n - type: ndcg_at_1\n value: 34.166999999999994\n - type: ndcg_at_10\n value: 41.577\n - type: ndcg_at_100\n value: 46.687\n - type: ndcg_at_1000\n value: 48.967\n - type: ndcg_at_3\n value: 37.177\n - type: ndcg_at_5\n value: 39.097\n - type: precision_at_1\n value: 34.166999999999994\n - type: precision_at_10\n value: 7.420999999999999\n - type: precision_at_100\n value: 1.165\n - type: precision_at_1000\n value: 0.154\n - type: precision_at_3\n value: 17.291999999999998\n - type: precision_at_5\n value: 12.166\n - type: recall_at_1\n value: 27.544\n - type: recall_at_10\n value: 51.99399999999999\n - type: recall_at_100\n value: 73.738\n - type: recall_at_1000\n value: 89.33\n - type: recall_at_3\n value: 39.179\n - type: recall_at_5\n value: 44.385999999999996\n - type: map_at_1\n value: 26.661\n - type: map_at_10\n value: 35.475\n - type: map_at_100\n value: 36.626999999999995\n - type: map_at_1000\n value: 36.741\n - type: map_at_3\n value: 32.818000000000005\n - type: map_at_5\n value: 34.397\n - type: mrr_at_1\n value: 32.647999999999996\n - type: mrr_at_10\n value: 40.784\n - type: mrr_at_100\n value: 41.602\n - type: mrr_at_1000\n value: 41.661\n - type: mrr_at_3\n value: 38.68\n - type: mrr_at_5\n value: 39.838\n - type: ndcg_at_1\n value: 32.647999999999996\n - type: ndcg_at_10\n value: 40.697\n - type: ndcg_at_100\n value: 45.799\n - type: ndcg_at_1000\n value: 48.235\n - type: ndcg_at_3\n value: 36.516\n - type: ndcg_at_5\n value: 38.515\n - type: precision_at_1\n value: 32.647999999999996\n - type: precision_at_10\n value: 7.202999999999999\n - type: precision_at_100\n value: 1.1360000000000001\n - type: precision_at_1000\n value: 0.151\n - type: precision_at_3\n value: 17.314\n - type: precision_at_5\n value: 12.145999999999999\n - type: recall_at_1\n value: 26.661\n - type: recall_at_10\n value: 50.995000000000005\n - type: recall_at_100\n value: 73.065\n - type: recall_at_1000\n value: 89.781\n - type: recall_at_3\n value: 39.073\n - type: recall_at_5\n value: 44.395\n - type: map_at_1\n value: 25.946583333333333\n - type: map_at_10\n value: 33.79725\n - type: map_at_100\n value: 34.86408333333333\n - type: map_at_1000\n value: 34.9795\n - type: map_at_3\n value: 31.259999999999998\n - type: map_at_5\n value: 32.71541666666666\n - type: mrr_at_1\n value: 30.863749999999996\n - type: mrr_at_10\n value: 37.99183333333333\n - type: mrr_at_100\n value: 38.790499999999994\n - type: mrr_at_1000\n value: 38.85575000000001\n - type: mrr_at_3\n value: 35.82083333333333\n - type: mrr_at_5\n value: 37.07533333333333\n - type: ndcg_at_1\n value: 30.863749999999996\n - type: ndcg_at_10\n value: 38.52141666666667\n - type: ndcg_at_100\n value: 43.17966666666667\n - type: ndcg_at_1000\n value: 45.64608333333333\n - type: ndcg_at_3\n value: 34.333000000000006\n - type: ndcg_at_5\n value: 36.34975\n - type: precision_at_1\n value: 30.863749999999996\n - type: precision_at_10\n value: 6.598999999999999\n - type: precision_at_100\n value: 1.0502500000000001\n - type: precision_at_1000\n value: 0.14400000000000002\n - type: precision_at_3\n value: 15.557583333333334\n - type: precision_at_5\n value: 11.020000000000001\n - type: recall_at_1\n value: 25.946583333333333\n - type: recall_at_10\n value: 48.36991666666666\n - type: recall_at_100\n value: 69.02408333333334\n - type: recall_at_1000\n value: 86.43858333333331\n - type: recall_at_3\n value: 36.4965\n - type: recall_at_5\n value: 41.76258333333334\n - type: map_at_1\n value: 22.431\n - type: map_at_10\n value: 28.889\n - type: map_at_100\n value: 29.642000000000003\n - type: map_at_1000\n value: 29.742\n - type: map_at_3\n value: 26.998\n - type: map_at_5\n value: 28.172000000000004\n - type: mrr_at_1\n value: 25.307000000000002\n - type: mrr_at_10\n value: 31.763\n - type: mrr_at_100\n value: 32.443\n - type: mrr_at_1000\n value: 32.531\n - type: mrr_at_3\n value: 29.959000000000003\n - type: mrr_at_5\n value: 31.063000000000002\n - type: ndcg_at_1\n value: 25.307000000000002\n - type: ndcg_at_10\n value: 32.586999999999996\n - type: ndcg_at_100\n value: 36.5\n - type: ndcg_at_1000\n value: 39.133\n - type: ndcg_at_3\n value: 29.25\n - type: ndcg_at_5\n value: 31.023\n - type: precision_at_1\n value: 25.307000000000002\n - type: precision_at_10\n value: 4.954\n - type: precision_at_100\n value: 0.747\n - type: precision_at_1000\n value: 0.104\n - type: precision_at_3\n value: 12.577\n - type: precision_at_5\n value: 8.741999999999999\n - type: recall_at_1\n value: 22.431\n - type: recall_at_10\n value: 41.134\n - type: recall_at_100\n value: 59.28600000000001\n - type: recall_at_1000\n value: 78.857\n - type: recall_at_3\n value: 31.926\n - type: recall_at_5\n value: 36.335\n - type: map_at_1\n value: 17.586\n - type: map_at_10\n value: 23.304\n - type: map_at_100\n value: 24.159\n - type: map_at_1000\n value: 24.281\n - type: map_at_3\n value: 21.316\n - type: map_at_5\n value: 22.383\n - type: mrr_at_1\n value: 21.645\n - type: mrr_at_10\n value: 27.365000000000002\n - type: mrr_at_100\n value: 28.108\n - type: mrr_at_1000\n value: 28.192\n - type: mrr_at_3\n value: 25.482\n - type: mrr_at_5\n value: 26.479999999999997\n - type: ndcg_at_1\n value: 21.645\n - type: ndcg_at_10\n value: 27.306\n - type: ndcg_at_100\n value: 31.496000000000002\n - type: ndcg_at_1000\n value: 34.53\n - type: ndcg_at_3\n value: 23.73\n - type: ndcg_at_5\n value: 25.294\n - type: precision_at_1\n value: 21.645\n - type: precision_at_10\n value: 4.797\n - type: precision_at_100\n value: 0.8059999999999999\n - type: precision_at_1000\n value: 0.121\n - type: precision_at_3\n value: 10.850999999999999\n - type: precision_at_5\n value: 7.736\n - type: recall_at_1\n value: 17.586\n - type: recall_at_10\n value: 35.481\n - type: recall_at_100\n value: 54.534000000000006\n - type: recall_at_1000\n value: 76.456\n - type: recall_at_3\n value: 25.335\n - type: recall_at_5\n value: 29.473\n - type: map_at_1\n value: 25.095\n - type: map_at_10\n value: 32.374\n - type: map_at_100\n value: 33.537\n - type: map_at_1000\n value: 33.634\n - type: map_at_3\n value: 30.089\n - type: map_at_5\n value: 31.433\n - type: mrr_at_1\n value: 29.198\n - type: mrr_at_10\n value: 36.01\n - type: mrr_at_100\n value: 37.022\n - type: mrr_at_1000\n value: 37.083\n - type: mrr_at_3\n value: 33.94\n - type: mrr_at_5\n value: 35.148\n - type: ndcg_at_1\n value: 29.198\n - type: ndcg_at_10\n value: 36.729\n - type: ndcg_at_100\n value: 42.114000000000004\n - type: ndcg_at_1000\n value: 44.592\n - type: ndcg_at_3\n value: 32.644\n - type: ndcg_at_5\n value: 34.652\n - type: precision_at_1\n value: 29.198\n - type: precision_at_10\n value: 5.970000000000001\n - type: precision_at_100\n value: 0.967\n - type: precision_at_1000\n value: 0.129\n - type: precision_at_3\n value: 14.396999999999998\n - type: precision_at_5\n value: 10.093\n - type: recall_at_1\n value: 25.095\n - type: recall_at_10\n value: 46.392\n - type: recall_at_100\n value: 69.706\n - type: recall_at_1000\n value: 87.738\n - type: recall_at_3\n value: 35.303000000000004\n - type: recall_at_5\n value: 40.441\n - type: map_at_1\n value: 26.857999999999997\n - type: map_at_10\n value: 34.066\n - type: map_at_100\n value: 35.671\n - type: map_at_1000\n value: 35.881\n - type: map_at_3\n value: 31.304\n - type: map_at_5\n value: 32.885\n - type: mrr_at_1\n value: 32.411\n - type: mrr_at_10\n value: 38.987\n - type: mrr_at_100\n value: 39.894\n - type: mrr_at_1000\n value: 39.959\n - type: mrr_at_3\n value: 36.626999999999995\n - type: mrr_at_5\n value: 38.011\n - type: ndcg_at_1\n value: 32.411\n - type: ndcg_at_10\n value: 39.208\n - type: ndcg_at_100\n value: 44.626\n - type: ndcg_at_1000\n value: 47.43\n - type: ndcg_at_3\n value: 35.091\n - type: ndcg_at_5\n value: 37.119\n - type: precision_at_1\n value: 32.411\n - type: precision_at_10\n value: 7.51\n - type: precision_at_100\n value: 1.486\n - type: precision_at_1000\n value: 0.234\n - type: precision_at_3\n value: 16.14\n - type: precision_at_5\n value: 11.976\n - type: recall_at_1\n value: 26.857999999999997\n - type: recall_at_10\n value: 47.407\n - type: recall_at_100\n value: 72.236\n - type: recall_at_1000\n value: 90.77\n - type: recall_at_3\n value: 35.125\n - type: recall_at_5\n value: 40.522999999999996\n - type: map_at_1\n value: 21.3\n - type: map_at_10\n value: 27.412999999999997\n - type: map_at_100\n value: 28.29\n - type: map_at_1000\n value: 28.398\n - type: map_at_3\n value: 25.169999999999998\n - type: map_at_5\n value: 26.496\n - type: mrr_at_1\n value: 23.29\n - type: mrr_at_10\n value: 29.215000000000003\n - type: mrr_at_100\n value: 30.073\n - type: mrr_at_1000\n value: 30.156\n - type: mrr_at_3\n value: 26.956000000000003\n - type: mrr_at_5\n value: 28.38\n - type: ndcg_at_1\n value: 23.29\n - type: ndcg_at_10\n value: 31.113000000000003\n - type: ndcg_at_100\n value: 35.701\n - type: ndcg_at_1000\n value: 38.505\n - type: ndcg_at_3\n value: 26.727\n - type: ndcg_at_5\n value: 29.037000000000003\n - type: precision_at_1\n value: 23.29\n - type: precision_at_10\n value: 4.787\n - type: precision_at_100\n value: 0.763\n - type: precision_at_1000\n value: 0.11100000000000002\n - type: precision_at_3\n value: 11.091\n - type: precision_at_5\n value: 7.985\n - type: recall_at_1\n value: 21.3\n - type: recall_at_10\n value: 40.782000000000004\n - type: recall_at_100\n value: 62.13999999999999\n - type: recall_at_1000\n value: 83.012\n - type: recall_at_3\n value: 29.131\n - type: recall_at_5\n value: 34.624\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 9.631\n - type: map_at_10\n value: 16.634999999999998\n - type: map_at_100\n value: 18.23\n - type: map_at_1000\n value: 18.419\n - type: map_at_3\n value: 13.66\n - type: map_at_5\n value: 15.173\n - type: mrr_at_1\n value: 21.368000000000002\n - type: mrr_at_10\n value: 31.56\n - type: mrr_at_100\n value: 32.58\n - type: mrr_at_1000\n value: 32.633\n - type: mrr_at_3\n value: 28.241\n - type: mrr_at_5\n value: 30.225\n - type: ndcg_at_1\n value: 21.368000000000002\n - type: ndcg_at_10\n value: 23.855999999999998\n - type: ndcg_at_100\n value: 30.686999999999998\n - type: ndcg_at_1000\n value: 34.327000000000005\n - type: ndcg_at_3\n value: 18.781\n - type: ndcg_at_5\n value: 20.73\n - type: precision_at_1\n value: 21.368000000000002\n - type: precision_at_10\n value: 7.564\n - type: precision_at_100\n value: 1.496\n - type: precision_at_1000\n value: 0.217\n - type: precision_at_3\n value: 13.876\n - type: precision_at_5\n value: 11.062\n - type: recall_at_1\n value: 9.631\n - type: recall_at_10\n value: 29.517\n - type: recall_at_100\n value: 53.452\n - type: recall_at_1000\n value: 74.115\n - type: recall_at_3\n value: 17.605999999999998\n - type: recall_at_5\n value: 22.505\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 8.885\n - type: map_at_10\n value: 18.798000000000002\n - type: map_at_100\n value: 26.316\n - type: map_at_1000\n value: 27.869\n - type: map_at_3\n value: 13.719000000000001\n - type: map_at_5\n value: 15.716\n - type: mrr_at_1\n value: 66\n - type: mrr_at_10\n value: 74.263\n - type: mrr_at_100\n value: 74.519\n - type: mrr_at_1000\n value: 74.531\n - type: mrr_at_3\n value: 72.458\n - type: mrr_at_5\n value: 73.321\n - type: ndcg_at_1\n value: 53.87499999999999\n - type: ndcg_at_10\n value: 40.355999999999995\n - type: ndcg_at_100\n value: 44.366\n - type: ndcg_at_1000\n value: 51.771\n - type: ndcg_at_3\n value: 45.195\n - type: ndcg_at_5\n value: 42.187000000000005\n - type: precision_at_1\n value: 66\n - type: precision_at_10\n value: 31.75\n - type: precision_at_100\n value: 10.11\n - type: precision_at_1000\n value: 1.9800000000000002\n - type: precision_at_3\n value: 48.167\n - type: precision_at_5\n value: 40.050000000000004\n - type: recall_at_1\n value: 8.885\n - type: recall_at_10\n value: 24.471999999999998\n - type: recall_at_100\n value: 49.669000000000004\n - type: recall_at_1000\n value: 73.383\n - type: recall_at_3\n value: 14.872\n - type: recall_at_5\n value: 18.262999999999998\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 45.18\n - type: f1\n value: 40.26878691789978\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 62.751999999999995\n - type: map_at_10\n value: 74.131\n - type: map_at_100\n value: 74.407\n - type: map_at_1000\n value: 74.423\n - type: map_at_3\n value: 72.329\n - type: map_at_5\n value: 73.555\n - type: mrr_at_1\n value: 67.282\n - type: mrr_at_10\n value: 78.292\n - type: mrr_at_100\n value: 78.455\n - type: mrr_at_1000\n value: 78.458\n - type: mrr_at_3\n value: 76.755\n - type: mrr_at_5\n value: 77.839\n - type: ndcg_at_1\n value: 67.282\n - type: ndcg_at_10\n value: 79.443\n - type: ndcg_at_100\n value: 80.529\n - type: ndcg_at_1000\n value: 80.812\n - type: ndcg_at_3\n value: 76.281\n - type: ndcg_at_5\n value: 78.235\n - type: precision_at_1\n value: 67.282\n - type: precision_at_10\n value: 10.078\n - type: precision_at_100\n value: 1.082\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 30.178\n - type: precision_at_5\n value: 19.232\n - type: recall_at_1\n value: 62.751999999999995\n - type: recall_at_10\n value: 91.521\n - type: recall_at_100\n value: 95.997\n - type: recall_at_1000\n value: 97.775\n - type: recall_at_3\n value: 83.131\n - type: recall_at_5\n value: 87.93299999999999\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 18.861\n - type: map_at_10\n value: 30.252000000000002\n - type: map_at_100\n value: 32.082\n - type: map_at_1000\n value: 32.261\n - type: map_at_3\n value: 25.909\n - type: map_at_5\n value: 28.296\n - type: mrr_at_1\n value: 37.346000000000004\n - type: mrr_at_10\n value: 45.802\n - type: mrr_at_100\n value: 46.611999999999995\n - type: mrr_at_1000\n value: 46.659\n - type: mrr_at_3\n value: 43.056\n - type: mrr_at_5\n value: 44.637\n - type: ndcg_at_1\n value: 37.346000000000004\n - type: ndcg_at_10\n value: 38.169\n - type: ndcg_at_100\n value: 44.864\n - type: ndcg_at_1000\n value: 47.974\n - type: ndcg_at_3\n value: 33.619\n - type: ndcg_at_5\n value: 35.317\n - type: precision_at_1\n value: 37.346000000000004\n - type: precision_at_10\n value: 10.693999999999999\n - type: precision_at_100\n value: 1.775\n - type: precision_at_1000\n value: 0.231\n - type: precision_at_3\n value: 22.325\n - type: precision_at_5\n value: 16.852\n - type: recall_at_1\n value: 18.861\n - type: recall_at_10\n value: 45.672000000000004\n - type: recall_at_100\n value: 70.60499999999999\n - type: recall_at_1000\n value: 89.216\n - type: recall_at_3\n value: 30.361\n - type: recall_at_5\n value: 36.998999999999995\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 37.852999999999994\n - type: map_at_10\n value: 59.961\n - type: map_at_100\n value: 60.78\n - type: map_at_1000\n value: 60.843\n - type: map_at_3\n value: 56.39999999999999\n - type: map_at_5\n value: 58.646\n - type: mrr_at_1\n value: 75.70599999999999\n - type: mrr_at_10\n value: 82.321\n - type: mrr_at_100\n value: 82.516\n - type: mrr_at_1000\n value: 82.525\n - type: mrr_at_3\n value: 81.317\n - type: mrr_at_5\n value: 81.922\n - type: ndcg_at_1\n value: 75.70599999999999\n - type: ndcg_at_10\n value: 68.557\n - type: ndcg_at_100\n value: 71.485\n - type: ndcg_at_1000\n value: 72.71600000000001\n - type: ndcg_at_3\n value: 63.524\n - type: ndcg_at_5\n value: 66.338\n - type: precision_at_1\n value: 75.70599999999999\n - type: precision_at_10\n value: 14.463000000000001\n - type: precision_at_100\n value: 1.677\n - type: precision_at_1000\n value: 0.184\n - type: precision_at_3\n value: 40.806\n - type: precision_at_5\n value: 26.709\n - type: recall_at_1\n value: 37.852999999999994\n - type: recall_at_10\n value: 72.316\n - type: recall_at_100\n value: 83.842\n - type: recall_at_1000\n value: 91.999\n - type: recall_at_3\n value: 61.209\n - type: recall_at_5\n value: 66.77199999999999\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 85.46039999999999\n - type: ap\n value: 79.9812521351881\n - type: f1\n value: 85.31722909702084\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: msmarco\n config: default\n split: dev\n revision: None\n metrics:\n - type: map_at_1\n value: 22.704\n - type: map_at_10\n value: 35.329\n - type: map_at_100\n value: 36.494\n - type: map_at_1000\n value: 36.541000000000004\n - type: map_at_3\n value: 31.476\n - type: map_at_5\n value: 33.731\n - type: mrr_at_1\n value: 23.294999999999998\n - type: mrr_at_10\n value: 35.859\n - type: mrr_at_100\n value: 36.968\n - type: mrr_at_1000\n value: 37.008\n - type: mrr_at_3\n value: 32.085\n - type: mrr_at_5\n value: 34.299\n - type: ndcg_at_1\n value: 23.324\n - type: ndcg_at_10\n value: 42.274\n - type: ndcg_at_100\n value: 47.839999999999996\n - type: ndcg_at_1000\n value: 48.971\n - type: ndcg_at_3\n value: 34.454\n - type: ndcg_at_5\n value: 38.464\n - type: precision_at_1\n value: 23.324\n - type: precision_at_10\n value: 6.648\n - type: precision_at_100\n value: 0.9440000000000001\n - type: precision_at_1000\n value: 0.104\n - type: precision_at_3\n value: 14.674999999999999\n - type: precision_at_5\n value: 10.850999999999999\n - type: recall_at_1\n value: 22.704\n - type: recall_at_10\n value: 63.660000000000004\n - type: recall_at_100\n value: 89.29899999999999\n - type: recall_at_1000\n value: 97.88900000000001\n - type: recall_at_3\n value: 42.441\n - type: recall_at_5\n value: 52.04\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 93.1326949384405\n - type: f1\n value: 92.89743579612082\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (de)\n type: mteb/mtop_domain\n config: de\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 89.62524654832347\n - type: f1\n value: 88.65106082263151\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (es)\n type: mteb/mtop_domain\n config: es\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 90.59039359573046\n - type: f1\n value: 90.31532892105662\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (fr)\n type: mteb/mtop_domain\n config: fr\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 86.21046038208581\n - type: f1\n value: 86.41459529813113\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (hi)\n type: mteb/mtop_domain\n config: hi\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 87.3180351380423\n - type: f1\n value: 86.71383078226444\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (th)\n type: mteb/mtop_domain\n config: th\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 86.24231464737792\n - type: f1\n value: 86.31845567592403\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 75.27131782945736\n - type: f1\n value: 57.52079940417103\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (de)\n type: mteb/mtop_intent\n config: de\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 71.2341504649197\n - type: f1\n value: 51.349951558039244\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (es)\n type: mteb/mtop_intent\n config: es\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 71.27418278852569\n - type: f1\n value: 50.1714985749095\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (fr)\n type: mteb/mtop_intent\n config: fr\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 67.68243031631694\n - type: f1\n value: 50.1066160836192\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (hi)\n type: mteb/mtop_intent\n config: hi\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 69.2362854069559\n - type: f1\n value: 48.821279948766424\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (th)\n type: mteb/mtop_intent\n config: th\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 71.71428571428571\n - type: f1\n value: 53.94611389496195\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (af)\n type: mteb/amazon_massive_intent\n config: af\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 59.97646267652992\n - type: f1\n value: 57.26797883561521\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (am)\n type: mteb/amazon_massive_intent\n config: am\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 53.65501008742435\n - type: f1\n value: 50.416258382177034\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (ar)\n type: mteb/amazon_massive_intent\n config: ar\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - 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type: accuracy\n value: 77.08137188971082\n - type: f1\n value: 76.78000685068261\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (es)\n type: mteb/amazon_massive_scenario\n config: es\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 71.5030262273033\n - type: f1\n value: 71.71620130425673\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (fa)\n type: mteb/amazon_massive_scenario\n config: fa\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.24546065904505\n - type: f1\n value: 69.07638311730359\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (fi)\n type: mteb/amazon_massive_scenario\n config: fi\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 69.12911903160726\n - type: f1\n value: 68.32651736539815\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (fr)\n type: mteb/amazon_massive_scenario\n config: fr\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 71.89307330195025\n - type: f1\n value: 71.33986549860187\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (he)\n type: mteb/amazon_massive_scenario\n config: he\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 67.44451916610626\n - type: f1\n value: 66.90192664503866\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (hi)\n type: mteb/amazon_massive_scenario\n config: hi\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 69.16274377942166\n - type: f1\n value: 68.01090953775066\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (hu)\n type: mteb/amazon_massive_scenario\n config: hu\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.75319435104237\n - type: f1\n value: 70.18035309201403\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (hy)\n type: mteb/amazon_massive_scenario\n config: hy\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 63.14391392064559\n - type: f1\n value: 61.48286540778145\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (id)\n type: mteb/amazon_massive_scenario\n config: id\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.70275722932078\n - type: f1\n value: 70.26164779846495\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (is)\n type: mteb/amazon_massive_scenario\n config: is\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 60.93813046402153\n - type: f1\n value: 58.8852862116525\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (it)\n type: mteb/amazon_massive_scenario\n config: it\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 72.320107599193\n - type: f1\n value: 72.19836409602924\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ja)\n type: mteb/amazon_massive_scenario\n config: ja\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 74.65366509751176\n - type: f1\n value: 74.55188288799579\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (jv)\n type: mteb/amazon_massive_scenario\n config: jv\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 59.694014794889036\n - type: f1\n value: 58.11353311721067\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ka)\n type: mteb/amazon_massive_scenario\n config: ka\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 54.37457969065231\n - type: f1\n value: 52.81306134311697\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (km)\n type: mteb/amazon_massive_scenario\n config: km\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 48.3086751849361\n - type: f1\n value: 45.396449765419376\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (kn)\n type: mteb/amazon_massive_scenario\n config: kn\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 62.151983860121064\n - type: f1\n value: 60.31762544281696\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ko)\n type: mteb/amazon_massive_scenario\n config: ko\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 72.44788164088769\n - type: f1\n value: 71.68150151736367\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (lv)\n type: mteb/amazon_massive_scenario\n config: lv\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 62.81439139206455\n - type: f1\n value: 62.06735559105593\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ml)\n type: mteb/amazon_massive_scenario\n config: ml\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 68.04303967720242\n - type: f1\n value: 66.68298851670133\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (mn)\n type: mteb/amazon_massive_scenario\n config: mn\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 61.43913920645595\n - type: f1\n value: 60.25605977560783\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ms)\n type: mteb/amazon_massive_scenario\n config: ms\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 66.90316072629456\n - type: f1\n value: 65.1325924692381\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (my)\n type: mteb/amazon_massive_scenario\n config: my\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 61.63752521856086\n - type: f1\n value: 59.14284778039585\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (nb)\n type: mteb/amazon_massive_scenario\n config: nb\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 71.63080026899797\n - type: f1\n value: 70.89771864626877\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (nl)\n type: mteb/amazon_massive_scenario\n config: nl\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 72.10827168796234\n - type: f1\n value: 71.71954219691159\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (pl)\n type: mteb/amazon_massive_scenario\n config: pl\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.59515803631471\n - type: f1\n value: 70.05040128099003\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (pt)\n type: mteb/amazon_massive_scenario\n config: pt\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.83389374579691\n - type: f1\n value: 70.84877936562735\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ro)\n type: mteb/amazon_massive_scenario\n config: ro\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 69.18628110289173\n - type: f1\n value: 68.97232927921841\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ru)\n type: mteb/amazon_massive_scenario\n config: ru\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 72.99260255548083\n - type: f1\n value: 72.85139492157732\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (sl)\n type: mteb/amazon_massive_scenario\n config: sl\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 65.26227303295225\n - type: f1\n value: 65.08833655469431\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (sq)\n type: mteb/amazon_massive_scenario\n config: sq\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 66.48621385339611\n - type: f1\n value: 64.43483199071298\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (sv)\n type: mteb/amazon_massive_scenario\n config: sv\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 73.14391392064559\n - type: f1\n value: 72.2580822579741\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (sw)\n type: mteb/amazon_massive_scenario\n config: sw\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 59.88567585743107\n - type: f1\n value: 58.3073765932569\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ta)\n type: mteb/amazon_massive_scenario\n config: ta\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 62.38399462004034\n - type: f1\n value: 60.82139544252606\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (te)\n type: mteb/amazon_massive_scenario\n config: te\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 62.58574310692671\n - type: f1\n value: 60.71443370385374\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (th)\n type: mteb/amazon_massive_scenario\n config: th\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 71.61398789509079\n - type: f1\n value: 70.99761812049401\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (tl)\n type: mteb/amazon_massive_scenario\n config: tl\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 62.73705447209146\n - type: f1\n value: 61.680849331794796\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (tr)\n type: mteb/amazon_massive_scenario\n config: tr\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 71.66778749159381\n - type: f1\n value: 71.17320646080115\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (ur)\n type: mteb/amazon_massive_scenario\n config: ur\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 64.640215198386\n - type: f1\n value: 63.301805157015444\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (vi)\n type: mteb/amazon_massive_scenario\n config: vi\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.00672494956288\n - type: f1\n value: 70.26005548582106\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (zh-CN)\n type: mteb/amazon_massive_scenario\n config: zh-CN\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 75.42030934767989\n - type: f1\n value: 75.2074842882598\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (zh-TW)\n type: mteb/amazon_massive_scenario\n config: zh-TW\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.69266980497646\n - type: f1\n value: 70.94103167391192\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 28.91697191169135\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 28.434000079573313\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 30.96683513343383\n - type: mrr\n value: 31.967364078714834\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 5.5280000000000005\n - type: map_at_10\n value: 11.793\n - type: map_at_100\n value: 14.496999999999998\n - type: map_at_1000\n value: 15.783\n - type: map_at_3\n value: 8.838\n - type: map_at_5\n value: 10.07\n - type: mrr_at_1\n value: 43.653\n - type: mrr_at_10\n value: 51.531000000000006\n - type: mrr_at_100\n value: 52.205\n - type: mrr_at_1000\n value: 52.242999999999995\n - type: mrr_at_3\n value: 49.431999999999995\n - type: mrr_at_5\n value: 50.470000000000006\n - type: ndcg_at_1\n value: 42.415000000000006\n - type: ndcg_at_10\n value: 32.464999999999996\n - type: ndcg_at_100\n value: 28.927999999999997\n - type: ndcg_at_1000\n value: 37.629000000000005\n - type: ndcg_at_3\n value: 37.845\n - type: ndcg_at_5\n value: 35.147\n - type: precision_at_1\n value: 43.653\n - type: precision_at_10\n value: 23.932000000000002\n - type: precision_at_100\n value: 7.17\n - type: precision_at_1000\n value: 1.967\n - type: precision_at_3\n value: 35.397\n - type: precision_at_5\n value: 29.907\n - type: recall_at_1\n value: 5.5280000000000005\n - type: recall_at_10\n value: 15.568000000000001\n - type: recall_at_100\n value: 28.54\n - type: recall_at_1000\n value: 59.864\n - type: recall_at_3\n value: 9.822000000000001\n - type: recall_at_5\n value: 11.726\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 37.041000000000004\n - type: map_at_10\n value: 52.664\n - type: map_at_100\n value: 53.477\n - type: map_at_1000\n value: 53.505\n - type: map_at_3\n value: 48.510999999999996\n - type: map_at_5\n value: 51.036\n - type: mrr_at_1\n value: 41.338\n - type: mrr_at_10\n value: 55.071000000000005\n - type: mrr_at_100\n value: 55.672\n - type: mrr_at_1000\n value: 55.689\n - type: mrr_at_3\n value: 51.82\n - type: mrr_at_5\n value: 53.852\n - type: ndcg_at_1\n value: 41.338\n - type: ndcg_at_10\n value: 60.01800000000001\n - type: ndcg_at_100\n value: 63.409000000000006\n - type: ndcg_at_1000\n value: 64.017\n - type: ndcg_at_3\n value: 52.44799999999999\n - type: ndcg_at_5\n value: 56.571000000000005\n - type: precision_at_1\n value: 41.338\n - type: precision_at_10\n value: 9.531\n - type: precision_at_100\n value: 1.145\n - type: precision_at_1000\n value: 0.12\n - type: precision_at_3\n value: 23.416\n - type: precision_at_5\n value: 16.46\n - type: recall_at_1\n value: 37.041000000000004\n - type: recall_at_10\n value: 79.76299999999999\n - type: recall_at_100\n value: 94.39\n - type: recall_at_1000\n value: 98.851\n - type: recall_at_3\n value: 60.465\n - type: recall_at_5\n value: 69.906\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 69.952\n - type: map_at_10\n value: 83.758\n - type: map_at_100\n value: 84.406\n - type: map_at_1000\n value: 84.425\n - type: map_at_3\n value: 80.839\n - type: map_at_5\n value: 82.646\n - type: mrr_at_1\n value: 80.62\n - type: mrr_at_10\n value: 86.947\n - type: mrr_at_100\n value: 87.063\n - type: mrr_at_1000\n value: 87.064\n - type: mrr_at_3\n value: 85.96000000000001\n - type: mrr_at_5\n value: 86.619\n - type: ndcg_at_1\n value: 80.63\n - type: ndcg_at_10\n value: 87.64800000000001\n - type: ndcg_at_100\n value: 88.929\n - type: ndcg_at_1000\n value: 89.054\n - type: ndcg_at_3\n value: 84.765\n - type: ndcg_at_5\n value: 86.291\n - type: precision_at_1\n value: 80.63\n - type: precision_at_10\n value: 13.314\n - type: precision_at_100\n value: 1.525\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.1\n - type: precision_at_5\n value: 24.372\n - type: recall_at_1\n value: 69.952\n - type: recall_at_10\n value: 94.955\n - type: recall_at_100\n value: 99.38\n - type: recall_at_1000\n value: 99.96000000000001\n - type: recall_at_3\n value: 86.60600000000001\n - type: recall_at_5\n value: 90.997\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 42.41329517878427\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 55.171278362748666\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 4.213\n - type: map_at_10\n value: 9.895\n - type: map_at_100\n value: 11.776\n - type: map_at_1000\n value: 12.084\n - type: map_at_3\n value: 7.2669999999999995\n - type: map_at_5\n value: 8.620999999999999\n - type: mrr_at_1\n value: 20.8\n - type: mrr_at_10\n value: 31.112000000000002\n - type: mrr_at_100\n value: 32.274\n - type: mrr_at_1000\n value: 32.35\n - type: mrr_at_3\n value: 28.133000000000003\n - type: mrr_at_5\n value: 29.892999999999997\n - type: ndcg_at_1\n value: 20.8\n - type: ndcg_at_10\n value: 17.163999999999998\n - type: ndcg_at_100\n value: 24.738\n - type: ndcg_at_1000\n value: 30.316\n - type: ndcg_at_3\n value: 16.665\n - type: ndcg_at_5\n value: 14.478\n - type: precision_at_1\n value: 20.8\n - type: precision_at_10\n value: 8.74\n - type: precision_at_100\n value: 1.963\n - type: precision_at_1000\n value: 0.33\n - type: precision_at_3\n value: 15.467\n - type: precision_at_5\n value: 12.6\n - type: recall_at_1\n value: 4.213\n - type: recall_at_10\n value: 17.698\n - type: recall_at_100\n value: 39.838\n - type: recall_at_1000\n value: 66.893\n - type: recall_at_3\n value: 9.418\n - type: recall_at_5\n value: 12.773000000000001\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 82.90453315738294\n - type: cos_sim_spearman\n value: 78.51197850080254\n - type: euclidean_pearson\n value: 80.09647123597748\n - type: euclidean_spearman\n value: 78.63548011514061\n - type: manhattan_pearson\n value: 80.10645285675231\n - type: manhattan_spearman\n value: 78.57861806068901\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 84.2616156846401\n - type: cos_sim_spearman\n value: 76.69713867850156\n - type: euclidean_pearson\n value: 77.97948563800394\n - type: euclidean_spearman\n value: 74.2371211567807\n - type: manhattan_pearson\n value: 77.69697879669705\n - type: manhattan_spearman\n value: 73.86529778022278\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 77.0293269315045\n - type: cos_sim_spearman\n value: 78.02555120584198\n - type: euclidean_pearson\n value: 78.25398100379078\n - type: euclidean_spearman\n value: 78.66963870599464\n - type: manhattan_pearson\n value: 78.14314682167348\n - type: manhattan_spearman\n value: 78.57692322969135\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 79.16989925136942\n - type: cos_sim_spearman\n value: 76.5996225327091\n - type: euclidean_pearson\n value: 77.8319003279786\n - type: euclidean_spearman\n value: 76.42824009468998\n - type: manhattan_pearson\n value: 77.69118862737736\n - type: manhattan_spearman\n value: 76.25568104762812\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 87.42012286935325\n - type: cos_sim_spearman\n value: 88.15654297884122\n - type: euclidean_pearson\n value: 87.34082819427852\n - type: euclidean_spearman\n value: 88.06333589547084\n - type: manhattan_pearson\n value: 87.25115596784842\n - type: manhattan_spearman\n value: 87.9559927695203\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 82.88222044996712\n - type: cos_sim_spearman\n value: 84.28476589061077\n - type: euclidean_pearson\n value: 83.17399758058309\n - type: euclidean_spearman\n value: 83.85497357244542\n - type: manhattan_pearson\n value: 83.0308397703786\n - type: manhattan_spearman\n value: 83.71554539935046\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (ko-ko)\n type: mteb/sts17-crosslingual-sts\n config: ko-ko\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 80.20682986257339\n - type: cos_sim_spearman\n value: 79.94567120362092\n - type: euclidean_pearson\n value: 79.43122480368902\n - type: euclidean_spearman\n value: 79.94802077264987\n - type: manhattan_pearson\n value: 79.32653021527081\n - type: manhattan_spearman\n value: 79.80961146709178\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (ar-ar)\n type: mteb/sts17-crosslingual-sts\n config: ar-ar\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 74.46578144394383\n - type: cos_sim_spearman\n value: 74.52496637472179\n - type: euclidean_pearson\n value: 72.2903807076809\n - type: euclidean_spearman\n value: 73.55549359771645\n - type: manhattan_pearson\n value: 72.09324837709393\n - type: manhattan_spearman\n value: 73.36743103606581\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-ar)\n type: mteb/sts17-crosslingual-sts\n config: en-ar\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 71.37272335116\n - type: cos_sim_spearman\n value: 71.26702117766037\n - type: euclidean_pearson\n value: 67.114829954434\n - type: euclidean_spearman\n value: 66.37938893947761\n - type: manhattan_pearson\n value: 66.79688574095246\n - type: manhattan_spearman\n value: 66.17292828079667\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-de)\n type: mteb/sts17-crosslingual-sts\n config: en-de\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 80.61016770129092\n - type: cos_sim_spearman\n value: 82.08515426632214\n - type: euclidean_pearson\n value: 80.557340361131\n - type: euclidean_spearman\n value: 80.37585812266175\n - type: manhattan_pearson\n value: 80.6782873404285\n - type: manhattan_spearman\n value: 80.6678073032024\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 87.00150745350108\n - type: cos_sim_spearman\n value: 87.83441972211425\n - type: euclidean_pearson\n value: 87.94826702308792\n - type: euclidean_spearman\n value: 87.46143974860725\n - type: manhattan_pearson\n value: 87.97560344306105\n - type: manhattan_spearman\n value: 87.5267102829796\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-tr)\n type: mteb/sts17-crosslingual-sts\n config: en-tr\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 64.76325252267235\n - type: cos_sim_spearman\n value: 63.32615095463905\n - type: euclidean_pearson\n value: 64.07920669155716\n - type: euclidean_spearman\n value: 61.21409893072176\n - type: manhattan_pearson\n value: 64.26308625680016\n - type: manhattan_spearman\n value: 61.2438185254079\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (es-en)\n type: mteb/sts17-crosslingual-sts\n config: es-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 75.82644463022595\n - type: cos_sim_spearman\n value: 76.50381269945073\n - type: euclidean_pearson\n value: 75.1328548315934\n - type: euclidean_spearman\n value: 75.63761139408453\n - type: manhattan_pearson\n value: 75.18610101241407\n - type: manhattan_spearman\n value: 75.30669266354164\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (es-es)\n type: mteb/sts17-crosslingual-sts\n config: es-es\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 87.49994164686832\n - type: cos_sim_spearman\n value: 86.73743986245549\n - type: euclidean_pearson\n value: 86.8272894387145\n - type: euclidean_spearman\n value: 85.97608491000507\n - type: manhattan_pearson\n value: 86.74960140396779\n - type: manhattan_spearman\n value: 85.79285984190273\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (fr-en)\n type: mteb/sts17-crosslingual-sts\n config: fr-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 79.58172210788469\n - type: cos_sim_spearman\n value: 80.17516468334607\n - type: euclidean_pearson\n value: 77.56537843470504\n - type: euclidean_spearman\n value: 77.57264627395521\n - type: manhattan_pearson\n value: 78.09703521695943\n - type: manhattan_spearman\n value: 78.15942760916954\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (it-en)\n type: mteb/sts17-crosslingual-sts\n config: it-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 79.7589932931751\n - type: cos_sim_spearman\n value: 80.15210089028162\n - type: euclidean_pearson\n value: 77.54135223516057\n - type: euclidean_spearman\n value: 77.52697996368764\n - type: manhattan_pearson\n value: 77.65734439572518\n - type: manhattan_spearman\n value: 77.77702992016121\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (nl-en)\n type: mteb/sts17-crosslingual-sts\n config: nl-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 79.16682365511267\n - type: cos_sim_spearman\n value: 79.25311267628506\n - type: euclidean_pearson\n value: 77.54882036762244\n - type: euclidean_spearman\n value: 77.33212935194827\n - type: manhattan_pearson\n value: 77.98405516064015\n - type: manhattan_spearman\n value: 77.85075717865719\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 59.10473294775917\n - type: cos_sim_spearman\n value: 61.82780474476838\n - type: euclidean_pearson\n value: 45.885111672377256\n - type: euclidean_spearman\n value: 56.88306351932454\n - type: manhattan_pearson\n value: 46.101218127323186\n - type: manhattan_spearman\n value: 56.80953694186333\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (de)\n type: mteb/sts22-crosslingual-sts\n config: de\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 45.781923079584146\n - type: cos_sim_spearman\n value: 55.95098449691107\n - type: euclidean_pearson\n value: 25.4571031323205\n - type: euclidean_spearman\n value: 49.859978118078935\n - type: manhattan_pearson\n value: 25.624938455041384\n - type: manhattan_spearman\n value: 49.99546185049401\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (es)\n type: mteb/sts22-crosslingual-sts\n config: es\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 60.00618133997907\n - type: cos_sim_spearman\n value: 66.57896677718321\n - type: euclidean_pearson\n value: 42.60118466388821\n - type: euclidean_spearman\n value: 62.8210759715209\n - type: manhattan_pearson\n value: 42.63446860604094\n - type: manhattan_spearman\n value: 62.73803068925271\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (pl)\n type: mteb/sts22-crosslingual-sts\n config: pl\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 28.460759121626943\n - type: cos_sim_spearman\n value: 34.13459007469131\n - type: euclidean_pearson\n value: 6.0917739325525195\n - type: euclidean_spearman\n value: 27.9947262664867\n - type: manhattan_pearson\n value: 6.16877864169911\n - type: manhattan_spearman\n value: 28.00664163971514\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (tr)\n type: mteb/sts22-crosslingual-sts\n config: tr\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 57.42546621771696\n - type: cos_sim_spearman\n value: 63.699663168970474\n - type: euclidean_pearson\n value: 38.12085278789738\n - type: euclidean_spearman\n value: 58.12329140741536\n - type: manhattan_pearson\n value: 37.97364549443335\n - type: manhattan_spearman\n value: 57.81545502318733\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (ar)\n type: mteb/sts22-crosslingual-sts\n config: ar\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 46.82241380954213\n - type: cos_sim_spearman\n value: 57.86569456006391\n - type: euclidean_pearson\n value: 31.80480070178813\n - type: euclidean_spearman\n value: 52.484000620130104\n - type: manhattan_pearson\n value: 31.952708554646097\n - type: manhattan_spearman\n value: 52.8560972356195\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (ru)\n type: mteb/sts22-crosslingual-sts\n config: ru\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 52.00447170498087\n - type: cos_sim_spearman\n value: 60.664116225735164\n - type: euclidean_pearson\n value: 33.87382555421702\n - type: euclidean_spearman\n value: 55.74649067458667\n - type: manhattan_pearson\n value: 33.99117246759437\n - type: manhattan_spearman\n value: 55.98749034923899\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (zh)\n type: mteb/sts22-crosslingual-sts\n config: zh\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 58.06497233105448\n - type: cos_sim_spearman\n value: 65.62968801135676\n - type: euclidean_pearson\n value: 47.482076613243905\n - type: euclidean_spearman\n value: 62.65137791498299\n - type: manhattan_pearson\n value: 47.57052626104093\n - type: manhattan_spearman\n value: 62.436916516613294\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (fr)\n type: mteb/sts22-crosslingual-sts\n config: fr\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 70.49397298562575\n - type: cos_sim_spearman\n value: 74.79604041187868\n - type: euclidean_pearson\n value: 49.661891561317795\n - type: euclidean_spearman\n value: 70.31535537621006\n - type: manhattan_pearson\n value: 49.553715741850006\n - type: manhattan_spearman\n value: 70.24779344636806\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (de-en)\n type: mteb/sts22-crosslingual-sts\n config: de-en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 55.640574515348696\n - type: cos_sim_spearman\n value: 54.927959317689\n - type: euclidean_pearson\n value: 29.00139666967476\n - type: euclidean_spearman\n value: 41.86386566971605\n - type: manhattan_pearson\n value: 29.47411067730344\n - type: manhattan_spearman\n value: 42.337438424952786\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (es-en)\n type: mteb/sts22-crosslingual-sts\n config: es-en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 68.14095292259312\n - type: cos_sim_spearman\n value: 73.99017581234789\n - type: euclidean_pearson\n value: 46.46304297872084\n - type: euclidean_spearman\n value: 60.91834114800041\n - type: manhattan_pearson\n value: 47.07072666338692\n - type: manhattan_spearman\n value: 61.70415727977926\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (it)\n type: mteb/sts22-crosslingual-sts\n config: it\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 73.27184653359575\n - type: cos_sim_spearman\n value: 77.76070252418626\n - type: euclidean_pearson\n value: 62.30586577544778\n - type: euclidean_spearman\n value: 75.14246629110978\n - type: manhattan_pearson\n value: 62.328196884927046\n - type: manhattan_spearman\n value: 75.1282792981433\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (pl-en)\n type: mteb/sts22-crosslingual-sts\n config: pl-en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 71.59448528829957\n - type: cos_sim_spearman\n value: 70.37277734222123\n - type: euclidean_pearson\n value: 57.63145565721123\n - type: euclidean_spearman\n value: 66.10113048304427\n - type: manhattan_pearson\n value: 57.18897811586808\n - type: manhattan_spearman\n value: 66.5595511215901\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (zh-en)\n type: mteb/sts22-crosslingual-sts\n config: zh-en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 66.37520607720838\n - type: cos_sim_spearman\n value: 69.92282148997948\n - type: euclidean_pearson\n value: 40.55768770125291\n - type: euclidean_spearman\n value: 55.189128944669605\n - type: manhattan_pearson\n value: 41.03566433468883\n - type: manhattan_spearman\n value: 55.61251893174558\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (es-it)\n type: mteb/sts22-crosslingual-sts\n config: es-it\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 57.791929533771835\n - type: cos_sim_spearman\n value: 66.45819707662093\n - type: euclidean_pearson\n value: 39.03686018511092\n - type: euclidean_spearman\n value: 56.01282695640428\n - type: manhattan_pearson\n value: 38.91586623619632\n - type: manhattan_spearman\n value: 56.69394943612747\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (de-fr)\n type: mteb/sts22-crosslingual-sts\n config: de-fr\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 47.82224468473866\n - type: cos_sim_spearman\n value: 59.467307194781164\n - type: euclidean_pearson\n value: 27.428459190256145\n - type: euclidean_spearman\n value: 60.83463107397519\n - type: manhattan_pearson\n value: 27.487391578496638\n - type: manhattan_spearman\n value: 61.281380460246496\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (de-pl)\n type: mteb/sts22-crosslingual-sts\n config: de-pl\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 16.306666792752644\n - type: cos_sim_spearman\n value: 39.35486427252405\n - type: euclidean_pearson\n value: -2.7887154897955435\n - type: euclidean_spearman\n value: 27.1296051831719\n - type: manhattan_pearson\n value: -3.202291270581297\n - type: manhattan_spearman\n value: 26.32895849218158\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (fr-pl)\n type: mteb/sts22-crosslingual-sts\n config: fr-pl\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 59.67006803805076\n - type: cos_sim_spearman\n value: 73.24670207647144\n - type: euclidean_pearson\n value: 46.91884681500483\n - type: euclidean_spearman\n value: 16.903085094570333\n - type: manhattan_pearson\n value: 46.88391675325812\n - type: manhattan_spearman\n value: 28.17180849095055\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 83.79555591223837\n - type: cos_sim_spearman\n value: 85.63658602085185\n - type: euclidean_pearson\n value: 85.22080894037671\n - type: euclidean_spearman\n value: 85.54113580167038\n - type: manhattan_pearson\n value: 85.1639505960118\n - type: manhattan_spearman\n value: 85.43502665436196\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 80.73900991689766\n - type: mrr\n value: 94.81624131133934\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 55.678000000000004\n - type: map_at_10\n value: 65.135\n - type: map_at_100\n value: 65.824\n - type: map_at_1000\n value: 65.852\n - type: map_at_3\n value: 62.736000000000004\n - type: map_at_5\n value: 64.411\n - type: mrr_at_1\n value: 58.333\n - type: mrr_at_10\n value: 66.5\n - type: mrr_at_100\n value: 67.053\n - type: mrr_at_1000\n value: 67.08\n - type: mrr_at_3\n value: 64.944\n - type: mrr_at_5\n value: 65.89399999999999\n - type: ndcg_at_1\n value: 58.333\n - type: ndcg_at_10\n value: 69.34700000000001\n - type: ndcg_at_100\n value: 72.32\n - type: ndcg_at_1000\n value: 73.014\n - type: ndcg_at_3\n value: 65.578\n - type: ndcg_at_5\n value: 67.738\n - type: precision_at_1\n value: 58.333\n - type: precision_at_10\n value: 9.033\n - type: precision_at_100\n value: 1.0670000000000002\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 25.444\n - type: precision_at_5\n value: 16.933\n - type: recall_at_1\n value: 55.678000000000004\n - type: recall_at_10\n value: 80.72200000000001\n - type: recall_at_100\n value: 93.93299999999999\n - type: recall_at_1000\n value: 99.333\n - type: recall_at_3\n value: 70.783\n - type: recall_at_5\n value: 75.978\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.74653465346535\n - type: cos_sim_ap\n value: 93.01476369929063\n - type: cos_sim_f1\n value: 86.93009118541033\n - type: cos_sim_precision\n value: 88.09034907597535\n - type: cos_sim_recall\n value: 85.8\n - type: dot_accuracy\n value: 99.22970297029703\n - type: dot_ap\n value: 51.58725659485144\n - type: dot_f1\n value: 53.51351351351352\n - type: dot_precision\n value: 58.235294117647065\n - type: dot_recall\n value: 49.5\n - type: euclidean_accuracy\n value: 99.74356435643564\n - type: euclidean_ap\n value: 92.40332894384368\n - type: euclidean_f1\n value: 86.97838109602817\n - type: euclidean_precision\n value: 87.46208291203236\n - type: euclidean_recall\n value: 86.5\n - type: manhattan_accuracy\n value: 99.73069306930694\n - type: manhattan_ap\n value: 92.01320815721121\n - type: manhattan_f1\n value: 86.4135864135864\n - type: manhattan_precision\n value: 86.32734530938124\n - type: manhattan_recall\n value: 86.5\n - type: max_accuracy\n value: 99.74653465346535\n - type: max_ap\n value: 93.01476369929063\n - type: max_f1\n value: 86.97838109602817\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 55.2660514302523\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 30.4637783572547\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 49.41377758357637\n - type: mrr\n value: 50.138451213818854\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 28.887846011166594\n - type: cos_sim_spearman\n value: 30.10823258355903\n - type: dot_pearson\n value: 12.888049550236385\n - type: dot_spearman\n value: 12.827495903098123\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.21\n - type: map_at_10\n value: 1.667\n - type: map_at_100\n value: 9.15\n - type: map_at_1000\n value: 22.927\n - type: map_at_3\n value: 0.573\n - type: map_at_5\n value: 0.915\n - type: mrr_at_1\n value: 80\n - type: mrr_at_10\n value: 87.167\n - type: mrr_at_100\n value: 87.167\n - type: mrr_at_1000\n value: 87.167\n - type: mrr_at_3\n value: 85.667\n - type: mrr_at_5\n value: 87.167\n - type: ndcg_at_1\n value: 76\n - type: ndcg_at_10\n value: 69.757\n - type: ndcg_at_100\n value: 52.402\n - type: ndcg_at_1000\n value: 47.737\n - type: ndcg_at_3\n value: 71.866\n - type: ndcg_at_5\n value: 72.225\n - type: precision_at_1\n value: 80\n - type: precision_at_10\n value: 75\n - type: precision_at_100\n value: 53.959999999999994\n - type: precision_at_1000\n value: 21.568\n - type: precision_at_3\n value: 76.667\n - type: precision_at_5\n value: 78\n - type: recall_at_1\n value: 0.21\n - type: recall_at_10\n value: 1.9189999999999998\n - type: recall_at_100\n value: 12.589\n - type: recall_at_1000\n value: 45.312000000000005\n - type: recall_at_3\n value: 0.61\n - type: recall_at_5\n value: 1.019\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (sqi-eng)\n type: mteb/tatoeba-bitext-mining\n config: sqi-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 92.10000000000001\n - type: f1\n value: 90.06\n - type: precision\n value: 89.17333333333333\n - type: recall\n value: 92.10000000000001\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (fry-eng)\n type: mteb/tatoeba-bitext-mining\n config: fry-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 56.06936416184971\n - type: f1\n value: 50.87508028259473\n - type: precision\n value: 48.97398843930635\n - type: recall\n value: 56.06936416184971\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (kur-eng)\n type: mteb/tatoeba-bitext-mining\n config: kur-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 57.3170731707317\n - type: f1\n value: 52.96080139372822\n - type: precision\n value: 51.67861124382864\n - type: recall\n value: 57.3170731707317\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (tur-eng)\n type: mteb/tatoeba-bitext-mining\n config: tur-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 94.3\n - type: f1\n value: 92.67333333333333\n - type: precision\n value: 91.90833333333333\n - type: recall\n value: 94.3\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (deu-eng)\n type: mteb/tatoeba-bitext-mining\n config: deu-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 97.7\n - type: f1\n value: 97.07333333333332\n - type: precision\n value: 96.79500000000002\n - type: recall\n value: 97.7\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (nld-eng)\n type: mteb/tatoeba-bitext-mining\n config: nld-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 94.69999999999999\n - type: f1\n value: 93.2\n - type: precision\n value: 92.48333333333333\n - type: recall\n value: 94.69999999999999\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (ron-eng)\n type: mteb/tatoeba-bitext-mining\n config: ron-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 92.9\n - type: f1\n value: 91.26666666666667\n - type: precision\n value: 90.59444444444445\n - type: recall\n value: 92.9\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (ang-eng)\n type: mteb/tatoeba-bitext-mining\n config: ang-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 34.32835820895522\n - type: f1\n value: 29.074180380150533\n - type: precision\n value: 28.068207322920596\n - type: recall\n value: 34.32835820895522\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (ido-eng)\n type: mteb/tatoeba-bitext-mining\n config: ido-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 78.5\n - type: f1\n value: 74.3945115995116\n - type: precision\n value: 72.82967843459222\n - type: recall\n value: 78.5\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (jav-eng)\n type: mteb/tatoeba-bitext-mining\n config: jav-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 66.34146341463415\n - type: f1\n value: 61.2469400518181\n - type: precision\n value: 59.63977756660683\n - type: recall\n value: 66.34146341463415\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (isl-eng)\n type: mteb/tatoeba-bitext-mining\n config: isl-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 80.9\n - type: f1\n value: 76.90349206349207\n - type: precision\n value: 75.32921568627451\n - type: recall\n value: 80.9\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (slv-eng)\n type: mteb/tatoeba-bitext-mining\n config: slv-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 84.93317132442284\n - type: f1\n value: 81.92519105034295\n - type: precision\n value: 80.71283920615635\n - type: recall\n value: 84.93317132442284\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (cym-eng)\n type: mteb/tatoeba-bitext-mining\n config: cym-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 71.1304347826087\n - type: f1\n value: 65.22394755003451\n - type: precision\n value: 62.912422360248435\n - type: recall\n value: 71.1304347826087\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (kaz-eng)\n type: mteb/tatoeba-bitext-mining\n config: kaz-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 79.82608695652173\n - type: f1\n value: 75.55693581780538\n - type: precision\n value: 73.79420289855072\n - type: recall\n value: 79.82608695652173\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (est-eng)\n type: mteb/tatoeba-bitext-mining\n config: est-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 74\n - type: f1\n value: 70.51022222222223\n - type: precision\n value: 69.29673599347512\n - type: recall\n value: 74\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (heb-eng)\n type: mteb/tatoeba-bitext-mining\n config: heb-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 78.7\n - type: f1\n value: 74.14238095238095\n - type: precision\n value: 72.27214285714285\n - type: recall\n value: 78.7\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (gla-eng)\n type: mteb/tatoeba-bitext-mining\n config: gla-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 48.97466827503016\n - type: f1\n value: 43.080330405420874\n - type: precision\n value: 41.36505499593557\n - type: recall\n value: 48.97466827503016\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (mar-eng)\n type: mteb/tatoeba-bitext-mining\n config: mar-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 89.60000000000001\n - type: f1\n value: 86.62333333333333\n - type: precision\n value: 85.225\n - type: recall\n value: 89.60000000000001\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (lat-eng)\n type: mteb/tatoeba-bitext-mining\n config: lat-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 45.2\n - type: f1\n value: 39.5761253006253\n - type: precision\n value: 37.991358436312\n - type: recall\n value: 45.2\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (bel-eng)\n type: mteb/tatoeba-bitext-mining\n config: bel-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 89.5\n - type: f1\n value: 86.70333333333333\n - type: precision\n value: 85.53166666666667\n - type: recall\n value: 89.5\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (pms-eng)\n type: mteb/tatoeba-bitext-mining\n config: pms-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 50.095238095238095\n - type: f1\n value: 44.60650460650461\n - type: precision\n value: 42.774116796477045\n - type: recall\n value: 50.095238095238095\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (gle-eng)\n type: mteb/tatoeba-bitext-mining\n config: gle-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 63.4\n - type: f1\n value: 58.35967261904762\n - type: precision\n value: 56.54857142857143\n - type: recall\n value: 63.4\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (pes-eng)\n type: mteb/tatoeba-bitext-mining\n config: pes-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - 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type: accuracy\n value: 87.7\n - type: f1\n value: 84.71\n - type: precision\n value: 83.46166666666667\n - type: recall\n value: 87.7\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (vie-eng)\n type: mteb/tatoeba-bitext-mining\n config: vie-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 95.8\n - type: f1\n value: 94.68333333333334\n - type: precision\n value: 94.13333333333334\n - type: recall\n value: 95.8\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (nno-eng)\n type: mteb/tatoeba-bitext-mining\n config: nno-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 85.39999999999999\n - type: f1\n value: 82.5577380952381\n - type: precision\n value: 81.36833333333334\n - type: recall\n value: 85.39999999999999\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (cha-eng)\n type: mteb/tatoeba-bitext-mining\n config: cha-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - 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type: accuracy\n value: 92.2\n - type: f1\n value: 89.95666666666666\n - type: precision\n value: 88.92833333333333\n - type: recall\n value: 92.2\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (amh-eng)\n type: mteb/tatoeba-bitext-mining\n config: amh-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 79.76190476190477\n - type: f1\n value: 74.93386243386244\n - type: precision\n value: 73.11011904761904\n - type: recall\n value: 79.76190476190477\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (pam-eng)\n type: mteb/tatoeba-bitext-mining\n config: pam-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 8.799999999999999\n - type: f1\n value: 6.921439712248537\n - type: precision\n value: 6.489885109680683\n - type: recall\n value: 8.799999999999999\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (hsb-eng)\n type: mteb/tatoeba-bitext-mining\n config: hsb-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 45.75569358178054\n - type: f1\n value: 40.34699501312631\n - type: precision\n value: 38.57886764719063\n - type: recall\n value: 45.75569358178054\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (srp-eng)\n type: mteb/tatoeba-bitext-mining\n config: srp-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 91.4\n - type: f1\n value: 89.08333333333333\n - type: precision\n value: 88.01666666666668\n - type: recall\n value: 91.4\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (epo-eng)\n type: mteb/tatoeba-bitext-mining\n config: epo-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 93.60000000000001\n - type: f1\n value: 92.06690476190477\n - type: precision\n value: 91.45095238095239\n - type: recall\n value: 93.60000000000001\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (kzj-eng)\n type: mteb/tatoeba-bitext-mining\n config: kzj-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 7.5\n - type: f1\n value: 6.200363129378736\n - type: precision\n value: 5.89115314822466\n - type: recall\n value: 7.5\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (awa-eng)\n type: mteb/tatoeba-bitext-mining\n config: awa-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 73.59307359307358\n - type: f1\n value: 68.38933553219267\n - type: precision\n value: 66.62698412698413\n - type: recall\n value: 73.59307359307358\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (fao-eng)\n type: mteb/tatoeba-bitext-mining\n config: fao-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 69.8473282442748\n - type: f1\n value: 64.72373682297346\n - type: precision\n value: 62.82834214131924\n - type: recall\n value: 69.8473282442748\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (mal-eng)\n type: mteb/tatoeba-bitext-mining\n config: mal-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 97.5254730713246\n - type: f1\n value: 96.72489082969432\n - type: precision\n value: 96.33672974284326\n - type: recall\n value: 97.5254730713246\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (ile-eng)\n type: mteb/tatoeba-bitext-mining\n config: ile-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 75.6\n - type: f1\n value: 72.42746031746033\n - type: precision\n value: 71.14036630036631\n - type: recall\n value: 75.6\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (bos-eng)\n type: mteb/tatoeba-bitext-mining\n config: bos-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 91.24293785310734\n - type: f1\n value: 88.86064030131826\n - type: precision\n value: 87.73540489642184\n - type: recall\n value: 91.24293785310734\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (cor-eng)\n type: mteb/tatoeba-bitext-mining\n config: cor-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 6.2\n - type: f1\n value: 4.383083659794954\n - type: precision\n value: 4.027861324289673\n - type: recall\n value: 6.2\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (cat-eng)\n type: mteb/tatoeba-bitext-mining\n config: cat-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 86.8\n - type: f1\n value: 84.09428571428572\n - type: precision\n value: 83.00333333333333\n - type: recall\n value: 86.8\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (eus-eng)\n type: mteb/tatoeba-bitext-mining\n config: eus-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 60.699999999999996\n - type: f1\n value: 56.1584972394755\n - type: precision\n value: 54.713456330903135\n - type: recall\n value: 60.699999999999996\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (yue-eng)\n type: mteb/tatoeba-bitext-mining\n config: yue-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 84.2\n - type: f1\n value: 80.66190476190475\n - type: precision\n value: 79.19690476190476\n - type: recall\n value: 84.2\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (swe-eng)\n type: mteb/tatoeba-bitext-mining\n config: swe-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 93.2\n - type: f1\n value: 91.33\n - type: precision\n value: 90.45\n - type: recall\n value: 93.2\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (dtp-eng)\n type: mteb/tatoeba-bitext-mining\n config: dtp-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 6.3\n - type: f1\n value: 5.126828976748276\n - type: precision\n value: 4.853614328966668\n - type: recall\n value: 6.3\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (kat-eng)\n type: mteb/tatoeba-bitext-mining\n config: kat-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 81.76943699731903\n - type: f1\n value: 77.82873739308057\n - type: precision\n value: 76.27622452019234\n - type: recall\n value: 81.76943699731903\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (jpn-eng)\n type: mteb/tatoeba-bitext-mining\n config: jpn-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 92.30000000000001\n - type: f1\n value: 90.29666666666665\n - type: precision\n value: 89.40333333333334\n - type: recall\n value: 92.30000000000001\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (csb-eng)\n type: mteb/tatoeba-bitext-mining\n config: csb-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 29.249011857707508\n - type: f1\n value: 24.561866096392947\n - type: precision\n value: 23.356583740215456\n - type: recall\n value: 29.249011857707508\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (xho-eng)\n type: mteb/tatoeba-bitext-mining\n config: xho-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 77.46478873239437\n - type: f1\n value: 73.23943661971832\n - type: precision\n value: 71.66666666666667\n - type: recall\n value: 77.46478873239437\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (orv-eng)\n type: mteb/tatoeba-bitext-mining\n config: orv-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 20.35928143712575\n - type: f1\n value: 15.997867865075824\n - type: precision\n value: 14.882104658301346\n - type: recall\n value: 20.35928143712575\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (ind-eng)\n type: mteb/tatoeba-bitext-mining\n config: ind-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 92.2\n - type: f1\n value: 90.25999999999999\n - type: precision\n value: 89.45333333333335\n - type: recall\n value: 92.2\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (tuk-eng)\n type: mteb/tatoeba-bitext-mining\n config: tuk-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - 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type: accuracy\n value: 88.27361563517914\n - type: f1\n value: 85.12486427795874\n - type: precision\n value: 83.71335504885994\n - type: recall\n value: 88.27361563517914\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (slk-eng)\n type: mteb/tatoeba-bitext-mining\n config: slk-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 88.6\n - type: f1\n value: 86.39928571428571\n - type: precision\n value: 85.4947557997558\n - type: recall\n value: 88.6\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (tgl-eng)\n type: mteb/tatoeba-bitext-mining\n config: tgl-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 86.5\n - type: f1\n value: 83.77952380952381\n - type: precision\n value: 82.67602564102565\n - type: recall\n value: 86.5\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (ast-eng)\n type: mteb/tatoeba-bitext-mining\n config: ast-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - 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type: accuracy\n value: 91.10000000000001\n - type: f1\n value: 88.88\n - type: precision\n value: 87.96333333333334\n - type: recall\n value: 91.10000000000001\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (tzl-eng)\n type: mteb/tatoeba-bitext-mining\n config: tzl-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 38.46153846153847\n - type: f1\n value: 34.43978243978244\n - type: precision\n value: 33.429487179487175\n - type: recall\n value: 38.46153846153847\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (urd-eng)\n type: mteb/tatoeba-bitext-mining\n config: urd-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 88.9\n - type: f1\n value: 86.19888888888887\n - type: precision\n value: 85.07440476190476\n - type: recall\n value: 88.9\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (ara-eng)\n type: mteb/tatoeba-bitext-mining\n config: ara-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 85.9\n - type: f1\n value: 82.58857142857143\n - type: precision\n value: 81.15666666666667\n - type: recall\n value: 85.9\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (kor-eng)\n type: mteb/tatoeba-bitext-mining\n config: kor-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 86.8\n - type: f1\n value: 83.36999999999999\n - type: precision\n value: 81.86833333333333\n - type: recall\n value: 86.8\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (yid-eng)\n type: mteb/tatoeba-bitext-mining\n config: yid-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 68.51415094339622\n - type: f1\n value: 63.195000099481234\n - type: precision\n value: 61.394033442972116\n - type: recall\n value: 68.51415094339622\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (fin-eng)\n type: mteb/tatoeba-bitext-mining\n config: fin-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 88.5\n - type: f1\n value: 86.14603174603175\n - type: precision\n value: 85.1162037037037\n - type: recall\n value: 88.5\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (tha-eng)\n type: mteb/tatoeba-bitext-mining\n config: tha-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 95.62043795620438\n - type: f1\n value: 94.40389294403892\n - type: precision\n value: 93.7956204379562\n - type: recall\n value: 95.62043795620438\n - task:\n type: BitextMining\n dataset:\n name: MTEB Tatoeba (wuu-eng)\n type: mteb/tatoeba-bitext-mining\n config: wuu-eng\n split: test\n revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553\n metrics:\n - type: accuracy\n value: 81.8\n - type: f1\n value: 78.6532178932179\n - type: precision\n value: 77.46348795840176\n - type: recall\n value: 81.8\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 2.603\n - type: map_at_10\n value: 8.5\n - type: map_at_100\n value: 12.985\n - type: map_at_1000\n value: 14.466999999999999\n - type: map_at_3\n value: 4.859999999999999\n - type: map_at_5\n value: 5.817\n - type: mrr_at_1\n value: 28.571\n - type: mrr_at_10\n value: 42.331\n - type: mrr_at_100\n value: 43.592999999999996\n - type: mrr_at_1000\n value: 43.592999999999996\n - type: mrr_at_3\n value: 38.435\n - type: mrr_at_5\n value: 39.966\n - type: ndcg_at_1\n value: 26.531\n - type: ndcg_at_10\n value: 21.353\n - type: ndcg_at_100\n value: 31.087999999999997\n - type: ndcg_at_1000\n value: 43.163000000000004\n - type: ndcg_at_3\n value: 22.999\n - type: ndcg_at_5\n value: 21.451\n - type: precision_at_1\n value: 28.571\n - type: precision_at_10\n value: 19.387999999999998\n - type: precision_at_100\n value: 6.265\n - type: precision_at_1000\n value: 1.4160000000000001\n - type: precision_at_3\n value: 24.490000000000002\n - type: precision_at_5\n value: 21.224\n - type: recall_at_1\n value: 2.603\n - type: recall_at_10\n value: 14.474\n - type: recall_at_100\n value: 40.287\n - type: recall_at_1000\n value: 76.606\n - type: recall_at_3\n value: 5.978\n - type: recall_at_5\n value: 7.819\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 69.7848\n - type: ap\n value: 13.661023167088224\n - type: f1\n value: 53.61686134460943\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 61.28183361629882\n - type: f1\n value: 61.55481034919965\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 35.972128420092396\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 85.59933241938367\n - type: cos_sim_ap\n value: 72.20760361208136\n - type: cos_sim_f1\n value: 66.4447731755424\n - type: cos_sim_precision\n value: 62.35539102267469\n - type: cos_sim_recall\n value: 71.10817941952506\n - type: dot_accuracy\n value: 78.98313166835548\n - type: dot_ap\n value: 44.492521645493795\n - type: dot_f1\n value: 45.814889336016094\n - type: dot_precision\n value: 37.02439024390244\n - type: dot_recall\n value: 60.07915567282321\n - type: euclidean_accuracy\n value: 85.3907134767837\n - type: euclidean_ap\n value: 71.53847289080343\n - type: euclidean_f1\n value: 65.95952206778834\n - type: euclidean_precision\n value: 61.31006346328196\n - type: euclidean_recall\n value: 71.37203166226914\n - type: manhattan_accuracy\n value: 85.40859510043511\n - type: manhattan_ap\n value: 71.49664104395515\n - type: manhattan_f1\n value: 65.98569969356485\n - type: manhattan_precision\n value: 63.928748144482924\n - type: manhattan_recall\n value: 68.17941952506597\n - type: max_accuracy\n value: 85.59933241938367\n - type: max_ap\n value: 72.20760361208136\n - type: max_f1\n value: 66.4447731755424\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 88.83261536073273\n - type: cos_sim_ap\n value: 85.48178133644264\n - type: cos_sim_f1\n value: 77.87816307403935\n - type: cos_sim_precision\n value: 75.88953021114926\n - type: cos_sim_recall\n value: 79.97382198952879\n - type: dot_accuracy\n value: 79.76287499514883\n - type: dot_ap\n value: 59.17438838475084\n - type: dot_f1\n value: 56.34566667855996\n - type: dot_precision\n value: 52.50349092359864\n - type: dot_recall\n value: 60.794579611949494\n - type: euclidean_accuracy\n value: 88.76857996662397\n - type: euclidean_ap\n value: 85.22764834359887\n - type: euclidean_f1\n value: 77.65379751543554\n - type: euclidean_precision\n value: 75.11152683839401\n - type: euclidean_recall\n value: 80.37419156144134\n - type: manhattan_accuracy\n value: 88.6987231730508\n - type: manhattan_ap\n value: 85.18907981724007\n - type: manhattan_f1\n value: 77.51967028849757\n - type: manhattan_precision\n value: 75.49992701795358\n - type: manhattan_recall\n value: 79.65044656606098\n - type: max_accuracy\n value: 88.83261536073273\n - type: max_ap\n value: 85.48178133644264\n - type: max_f1\n value: 77.87816307403935\n---\n\n# dinab/multilingual-e5-base-Q4_K_M-GGUF\nThis model was converted to GGUF format from [`intfloat/multilingual-e5-base`](https://huggingface.co/intfloat/multilingual-e5-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.\nRefer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-base) for more details on the model.\n\n## Use with llama.cpp\nInstall llama.cpp through brew (works on Mac and Linux)\n\n```bash\nbrew install llama.cpp\n\n```\nInvoke the llama.cpp server or the CLI.\n\n### CLI:\n```bash\nllama-cli --hf-repo dinab/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -p \"The meaning to life and the universe is\"\n```\n\n### Server:\n```bash\nllama-server --hf-repo dinab/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -c 2048\n```\n\nNote: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.\n\nStep 1: Clone llama.cpp from GitHub.\n```\ngit clone https://github.com/ggerganov/llama.cpp\n```\n\nStep 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).\n```\ncd llama.cpp && LLAMA_CURL=1 make\n```\n\nStep 3: Run inference through the main binary.\n```\n./llama-cli --hf-repo dinab/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -p \"The meaning to life and the universe is\"\n```\nor \n```\n./llama-server --hf-repo dinab/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -c 2048\n```\n",
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"base_model:intfloat/multilingual-e5-base",
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Source payload excerpt (from Hugging Face API)
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