Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:111470
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/model-b-structured with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/model-b-structured with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/model-b-structured") sentences = [ "when was the first elephant brought to america", "Old Bet The first elephant brought to the United States was in 1796, aboard the America which set sail from Calcutta for New York on December 3, 1795.[4] However, it is not certain that this was Old Bet.[2] The first references to Old Bet start in 1804 in Boston as part of a menagerie.[1] In 1808, while residing in Somers, New York, Hachaliah Bailey purchased the menagerie elephant for $1,000 and named it \"Old Bet\".[5][6]", "Cronus Rhea secretly gave birth to Zeus in Crete, and handed Cronus a stone wrapped in swaddling clothes, also known as the Omphalos Stone, which he promptly swallowed, thinking that it was his son.", "Renal artery One or two accessory renal arteries are frequently found, especially on the left side since they usually arise from the aorta, and may come off above (more common) or below the main artery. Instead of entering the kidney at the hilus, they usually pierce the upper or lower part of the organ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 11,805 Bytes
e376fbf 2173df4 e33c413 2173df4 e33c413 2173df4 e376fbf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 | {
"nano_beir": {
"NanoClimateFEVER_cosine_accuracy@1": 0.02,
"NanoClimateFEVER_cosine_accuracy@3": 0.12,
"NanoClimateFEVER_cosine_accuracy@5": 0.22,
"NanoClimateFEVER_cosine_accuracy@10": 0.26,
"NanoClimateFEVER_cosine_precision@1": 0.02,
"NanoClimateFEVER_cosine_precision@3": 0.04666666666666666,
"NanoClimateFEVER_cosine_precision@5": 0.04800000000000001,
"NanoClimateFEVER_cosine_precision@10": 0.028000000000000004,
"NanoClimateFEVER_cosine_recall@1": 0.01,
"NanoClimateFEVER_cosine_recall@3": 0.07666666666666666,
"NanoClimateFEVER_cosine_recall@5": 0.11,
"NanoClimateFEVER_cosine_recall@10": 0.15,
"NanoClimateFEVER_cosine_ndcg@10": 0.08848514491479977,
"NanoClimateFEVER_cosine_mrr@10": 0.09155555555555556,
"NanoClimateFEVER_cosine_map@100": 0.062031025210026416,
"NanoDBPedia_cosine_accuracy@1": 0.64,
"NanoDBPedia_cosine_accuracy@3": 0.84,
"NanoDBPedia_cosine_accuracy@5": 0.88,
"NanoDBPedia_cosine_accuracy@10": 0.9,
"NanoDBPedia_cosine_precision@1": 0.64,
"NanoDBPedia_cosine_precision@3": 0.5533333333333333,
"NanoDBPedia_cosine_precision@5": 0.508,
"NanoDBPedia_cosine_precision@10": 0.42800000000000005,
"NanoDBPedia_cosine_recall@1": 0.07405822477207483,
"NanoDBPedia_cosine_recall@3": 0.14372311265072168,
"NanoDBPedia_cosine_recall@5": 0.21105435859646549,
"NanoDBPedia_cosine_recall@10": 0.2909207094781485,
"NanoDBPedia_cosine_ndcg@10": 0.5337363068424135,
"NanoDBPedia_cosine_mrr@10": 0.7428571428571428,
"NanoDBPedia_cosine_map@100": 0.3891072772432914,
"NanoFEVER_cosine_accuracy@1": 0.36,
"NanoFEVER_cosine_accuracy@3": 0.42,
"NanoFEVER_cosine_accuracy@5": 0.42,
"NanoFEVER_cosine_accuracy@10": 0.46,
"NanoFEVER_cosine_precision@1": 0.36,
"NanoFEVER_cosine_precision@3": 0.13999999999999999,
"NanoFEVER_cosine_precision@5": 0.084,
"NanoFEVER_cosine_precision@10": 0.048,
"NanoFEVER_cosine_recall@1": 0.36,
"NanoFEVER_cosine_recall@3": 0.41,
"NanoFEVER_cosine_recall@5": 0.41,
"NanoFEVER_cosine_recall@10": 0.46,
"NanoFEVER_cosine_ndcg@10": 0.4073423950239124,
"NanoFEVER_cosine_mrr@10": 0.39166666666666666,
"NanoFEVER_cosine_map@100": 0.39632722868719533,
"NanoFiQA2018_cosine_accuracy@1": 0.16,
"NanoFiQA2018_cosine_accuracy@3": 0.26,
"NanoFiQA2018_cosine_accuracy@5": 0.32,
"NanoFiQA2018_cosine_accuracy@10": 0.38,
"NanoFiQA2018_cosine_precision@1": 0.16,
"NanoFiQA2018_cosine_precision@3": 0.10666666666666666,
"NanoFiQA2018_cosine_precision@5": 0.096,
"NanoFiQA2018_cosine_precision@10": 0.068,
"NanoFiQA2018_cosine_recall@1": 0.06188888888888889,
"NanoFiQA2018_cosine_recall@3": 0.12674603174603175,
"NanoFiQA2018_cosine_recall@5": 0.17413492063492064,
"NanoFiQA2018_cosine_recall@10": 0.25515873015873014,
"NanoFiQA2018_cosine_ndcg@10": 0.1898252759944158,
"NanoFiQA2018_cosine_mrr@10": 0.22585714285714287,
"NanoFiQA2018_cosine_map@100": 0.15950890524795974,
"NanoHotpotQA_cosine_accuracy@1": 0.66,
"NanoHotpotQA_cosine_accuracy@3": 0.86,
"NanoHotpotQA_cosine_accuracy@5": 0.9,
"NanoHotpotQA_cosine_accuracy@10": 0.94,
"NanoHotpotQA_cosine_precision@1": 0.66,
"NanoHotpotQA_cosine_precision@3": 0.38666666666666666,
"NanoHotpotQA_cosine_precision@5": 0.272,
"NanoHotpotQA_cosine_precision@10": 0.142,
"NanoHotpotQA_cosine_recall@1": 0.33,
"NanoHotpotQA_cosine_recall@3": 0.58,
"NanoHotpotQA_cosine_recall@5": 0.68,
"NanoHotpotQA_cosine_recall@10": 0.71,
"NanoHotpotQA_cosine_ndcg@10": 0.6584980003699181,
"NanoHotpotQA_cosine_mrr@10": 0.775079365079365,
"NanoHotpotQA_cosine_map@100": 0.5871412173250823,
"NanoMSMARCO_cosine_accuracy@1": 0.38,
"NanoMSMARCO_cosine_accuracy@3": 0.64,
"NanoMSMARCO_cosine_accuracy@5": 0.76,
"NanoMSMARCO_cosine_accuracy@10": 0.84,
"NanoMSMARCO_cosine_precision@1": 0.38,
"NanoMSMARCO_cosine_precision@3": 0.21333333333333335,
"NanoMSMARCO_cosine_precision@5": 0.15200000000000002,
"NanoMSMARCO_cosine_precision@10": 0.08399999999999999,
"NanoMSMARCO_cosine_recall@1": 0.38,
"NanoMSMARCO_cosine_recall@3": 0.64,
"NanoMSMARCO_cosine_recall@5": 0.76,
"NanoMSMARCO_cosine_recall@10": 0.84,
"NanoMSMARCO_cosine_ndcg@10": 0.6071739753451822,
"NanoMSMARCO_cosine_mrr@10": 0.5318571428571428,
"NanoMSMARCO_cosine_map@100": 0.5383857612227555,
"NanoNFCorpus_cosine_accuracy@1": 0.46,
"NanoNFCorpus_cosine_accuracy@3": 0.54,
"NanoNFCorpus_cosine_accuracy@5": 0.64,
"NanoNFCorpus_cosine_accuracy@10": 0.7,
"NanoNFCorpus_cosine_precision@1": 0.46,
"NanoNFCorpus_cosine_precision@3": 0.37999999999999995,
"NanoNFCorpus_cosine_precision@5": 0.33199999999999996,
"NanoNFCorpus_cosine_precision@10": 0.282,
"NanoNFCorpus_cosine_recall@1": 0.031191419545013095,
"NanoNFCorpus_cosine_recall@3": 0.05157072806890092,
"NanoNFCorpus_cosine_recall@5": 0.06863574662214718,
"NanoNFCorpus_cosine_recall@10": 0.09991138318659122,
"NanoNFCorpus_cosine_ndcg@10": 0.32672780953059594,
"NanoNFCorpus_cosine_mrr@10": 0.5299365079365078,
"NanoNFCorpus_cosine_map@100": 0.1359241030015973,
"NanoNQ_cosine_accuracy@1": 0.58,
"NanoNQ_cosine_accuracy@3": 0.74,
"NanoNQ_cosine_accuracy@5": 0.8,
"NanoNQ_cosine_accuracy@10": 0.84,
"NanoNQ_cosine_precision@1": 0.58,
"NanoNQ_cosine_precision@3": 0.25333333333333335,
"NanoNQ_cosine_precision@5": 0.16399999999999998,
"NanoNQ_cosine_precision@10": 0.092,
"NanoNQ_cosine_recall@1": 0.55,
"NanoNQ_cosine_recall@3": 0.7,
"NanoNQ_cosine_recall@5": 0.74,
"NanoNQ_cosine_recall@10": 0.81,
"NanoNQ_cosine_ndcg@10": 0.6914852313456867,
"NanoNQ_cosine_mrr@10": 0.6720555555555556,
"NanoNQ_cosine_map@100": 0.6484054934853453,
"NanoQuoraRetrieval_cosine_accuracy@1": 0.6,
"NanoQuoraRetrieval_cosine_accuracy@3": 0.7,
"NanoQuoraRetrieval_cosine_accuracy@5": 0.74,
"NanoQuoraRetrieval_cosine_accuracy@10": 0.84,
"NanoQuoraRetrieval_cosine_precision@1": 0.6,
"NanoQuoraRetrieval_cosine_precision@3": 0.2733333333333334,
"NanoQuoraRetrieval_cosine_precision@5": 0.17999999999999997,
"NanoQuoraRetrieval_cosine_precision@10": 0.10399999999999998,
"NanoQuoraRetrieval_cosine_recall@1": 0.5433333333333333,
"NanoQuoraRetrieval_cosine_recall@3": 0.674,
"NanoQuoraRetrieval_cosine_recall@5": 0.708,
"NanoQuoraRetrieval_cosine_recall@10": 0.8053333333333333,
"NanoQuoraRetrieval_cosine_ndcg@10": 0.6910237122615918,
"NanoQuoraRetrieval_cosine_mrr@10": 0.6630238095238096,
"NanoQuoraRetrieval_cosine_map@100": 0.6556498327329501,
"NanoSCIDOCS_cosine_accuracy@1": 0.42,
"NanoSCIDOCS_cosine_accuracy@3": 0.56,
"NanoSCIDOCS_cosine_accuracy@5": 0.62,
"NanoSCIDOCS_cosine_accuracy@10": 0.78,
"NanoSCIDOCS_cosine_precision@1": 0.42,
"NanoSCIDOCS_cosine_precision@3": 0.2533333333333333,
"NanoSCIDOCS_cosine_precision@5": 0.18799999999999997,
"NanoSCIDOCS_cosine_precision@10": 0.148,
"NanoSCIDOCS_cosine_recall@1": 0.08866666666666667,
"NanoSCIDOCS_cosine_recall@3": 0.15866666666666668,
"NanoSCIDOCS_cosine_recall@5": 0.19466666666666665,
"NanoSCIDOCS_cosine_recall@10": 0.30466666666666664,
"NanoSCIDOCS_cosine_ndcg@10": 0.29680405295986745,
"NanoSCIDOCS_cosine_mrr@10": 0.5155714285714287,
"NanoSCIDOCS_cosine_map@100": 0.22732555154891504,
"NanoArguAna_cosine_accuracy@1": 0.22,
"NanoArguAna_cosine_accuracy@3": 0.56,
"NanoArguAna_cosine_accuracy@5": 0.76,
"NanoArguAna_cosine_accuracy@10": 0.9,
"NanoArguAna_cosine_precision@1": 0.22,
"NanoArguAna_cosine_precision@3": 0.18666666666666668,
"NanoArguAna_cosine_precision@5": 0.15200000000000002,
"NanoArguAna_cosine_precision@10": 0.08999999999999998,
"NanoArguAna_cosine_recall@1": 0.22,
"NanoArguAna_cosine_recall@3": 0.56,
"NanoArguAna_cosine_recall@5": 0.76,
"NanoArguAna_cosine_recall@10": 0.9,
"NanoArguAna_cosine_ndcg@10": 0.5430980629264645,
"NanoArguAna_cosine_mrr@10": 0.43080158730158724,
"NanoArguAna_cosine_map@100": 0.43563881673881677,
"NanoSciFact_cosine_accuracy@1": 0.3,
"NanoSciFact_cosine_accuracy@3": 0.46,
"NanoSciFact_cosine_accuracy@5": 0.58,
"NanoSciFact_cosine_accuracy@10": 0.66,
"NanoSciFact_cosine_precision@1": 0.3,
"NanoSciFact_cosine_precision@3": 0.1733333333333333,
"NanoSciFact_cosine_precision@5": 0.132,
"NanoSciFact_cosine_precision@10": 0.076,
"NanoSciFact_cosine_recall@1": 0.265,
"NanoSciFact_cosine_recall@3": 0.45,
"NanoSciFact_cosine_recall@5": 0.575,
"NanoSciFact_cosine_recall@10": 0.65,
"NanoSciFact_cosine_ndcg@10": 0.4673023534460552,
"NanoSciFact_cosine_mrr@10": 0.41438095238095246,
"NanoSciFact_cosine_map@100": 0.41151414702043504,
"NanoTouche2020_cosine_accuracy@1": 0.4897959183673469,
"NanoTouche2020_cosine_accuracy@3": 0.6530612244897959,
"NanoTouche2020_cosine_accuracy@5": 0.7142857142857143,
"NanoTouche2020_cosine_accuracy@10": 0.7959183673469388,
"NanoTouche2020_cosine_precision@1": 0.4897959183673469,
"NanoTouche2020_cosine_precision@3": 0.3877551020408163,
"NanoTouche2020_cosine_precision@5": 0.34693877551020413,
"NanoTouche2020_cosine_precision@10": 0.3081632653061224,
"NanoTouche2020_cosine_recall@1": 0.025396389616293827,
"NanoTouche2020_cosine_recall@3": 0.06154426522223343,
"NanoTouche2020_cosine_recall@5": 0.09456739986624485,
"NanoTouche2020_cosine_recall@10": 0.1694155640852146,
"NanoTouche2020_cosine_ndcg@10": 0.33935182898102295,
"NanoTouche2020_cosine_mrr@10": 0.5771541950113378,
"NanoTouche2020_cosine_map@100": 0.24098827105394732,
"NanoBEIR_mean_cosine_accuracy@1": 0.4069073783359497,
"NanoBEIR_mean_cosine_accuracy@3": 0.5656200941915228,
"NanoBEIR_mean_cosine_accuracy@5": 0.6426373626373626,
"NanoBEIR_mean_cosine_accuracy@10": 0.7150706436420722,
"NanoBEIR_mean_cosine_precision@1": 0.4069073783359497,
"NanoBEIR_mean_cosine_precision@3": 0.25803244374672946,
"NanoBEIR_mean_cosine_precision@5": 0.20422605965463111,
"NanoBEIR_mean_cosine_precision@10": 0.14601255886970174,
"NanoBEIR_mean_cosine_recall@1": 0.22611807098632852,
"NanoBEIR_mean_cosine_recall@3": 0.35637826700163233,
"NanoBEIR_mean_cosine_recall@5": 0.422004545568188,
"NanoBEIR_mean_cosine_recall@10": 0.49580049130066806,
"NanoBEIR_mean_cosine_ndcg@10": 0.44929647307245585,
"NanoBEIR_mean_cosine_mrr@10": 0.5047536193964766,
"NanoBEIR_mean_cosine_map@100": 0.3759959715783321
},
"beir_touche2020": {
"BeIR-touche2020-subset-test_cosine_accuracy@1": 0.6122448979591837,
"BeIR-touche2020-subset-test_cosine_accuracy@3": 0.7959183673469388,
"BeIR-touche2020-subset-test_cosine_accuracy@5": 0.8163265306122449,
"BeIR-touche2020-subset-test_cosine_accuracy@10": 0.8775510204081632,
"BeIR-touche2020-subset-test_cosine_precision@1": 0.6122448979591837,
"BeIR-touche2020-subset-test_cosine_precision@3": 0.5986394557823129,
"BeIR-touche2020-subset-test_cosine_precision@5": 0.5346938775510205,
"BeIR-touche2020-subset-test_cosine_precision@10": 0.510204081632653,
"BeIR-touche2020-subset-test_cosine_recall@1": 0.013676157134653104,
"BeIR-touche2020-subset-test_cosine_recall@3": 0.04019009495215129,
"BeIR-touche2020-subset-test_cosine_recall@5": 0.05972908597066891,
"BeIR-touche2020-subset-test_cosine_recall@10": 0.11372605615518665,
"BeIR-touche2020-subset-test_cosine_ndcg@10": 0.5336230219389327,
"BeIR-touche2020-subset-test_cosine_mrr@10": 0.7097181729834792,
"BeIR-touche2020-subset-test_cosine_map@100": 0.23434719987479757
}
} |