base_model: answerdotai/ModernBERT-base base_model_relation: finetune datasets:

• sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 language:
• en library_name: sentence-transformers metrics:
• cosine_accuracy pipeline_tag: sentence-similarity tags:
• sentence-transformers
• sentence-similarity
• feature-extraction
• generated_from_trainer
• dataset_size:11662655
• loss:CachedMultipleNegativesRankingLoss widget:
• source_sentence: what county is lyndhurst, ohio in sentences:
  • This article is about the song written by Kenneth Gamble, Leon Huff and Cary Gilbert. For the Tina Turner song, see Don't Leave Me This Way (Tina Turner song). Don't Leave Me This Way is a song written by Kenneth Gamble, Leon Huff and Cary Gilbert. First charting as a hit for Harold Melvin & the Blue Notes featuring Teddy Pendergrass, an act on Gamble & Huff's Philadelphia International label in 1975, Don't Leave Me This Way was later a huge disco hit for Motown artist Thelma Houston in 1977.
  • "Lyndhurst is a city in Cuyahoga County, Ohio, United States. The population was
    \ 14,001 at the 2010 census. Lyndhurst is located in northeastern Ohio, and is
    \ a suburb of Cleveland. A small part of Lyndhurst was originally part of Mayfield
    \ Township. It used to be called Euclidville before Lyndhurst was chosen. Lyndhurst
    \ is located at 41°31â\x80²17â\x80³N 81°29â\x80²25â\x80³W / 41.52139°N 81.49028°W
    \ / 41.52139; -81.49028 (41.521352, -81.490141)."
  • Welcome to Trumbull County... Trumbull County, the county seat, located in Warren, Ohio, consists of a combination of both urban and rural communities situated in the northeast corner of Ohio. It is situated roughly between the Youngstown, Cleveland and Akron corridors.
• source_sentence: who founded the american graphophone company sentences:
  • In 1886, Graham Bell and Charles Sumner Tainter founded the American Graphophone Company to distribute and sell graphophones in the US and Canada under license from the Volta Graphophone Company. In 1890, the American Graphophone Company stopped production of new phonographs due to sagging orders.
  • ShelfGenie How much does a ShelfGenie franchise cost? ShelfGenie has a franchise fee of up to $45,000, with a total initial investment range of $70,100 to $107,750. Local ShelfGenie franchise opportunities. ShelfGenie is looking to grow in a number of cities around the country. To find out if there's a franchise opportunity in your city, unlock more information.
  • "A+E Networks. The technology that made the modern music business possible came
    \ into existence in the New Jersey laboratory where Thomas Alva Edison created
    \ the first device to both record sound and play it back. He was awarded U.S.
    \ Patent No. 200,521 for his inventionâ\x80\x93the phonographâ\x80\x93on this
    \ day in 1878."
• source_sentence: is housekeeping camp flooded? sentences:
  • 'What is the importance of housekeeping at work? A: Workplace housekeeping promotes sanitation, safety, organization and productivity. It also boosts morale. Daily housekeeping maintenance keeps the workplac... Full Answer >'
  • The back patio area of a cabin is partially submerged in flood water at Housekeeping Camp on Monday, Jan. 9, 2017, in Yosemite National Park. The Merced River, swollen with storm runoff, crested at 12.7 feet at 4 a.m. SILVIA FLORES sflores@fresnobee.com.
  • "1 Bake for 8 minutes, then rotate the pan and check the underside of the bagels.
    \ 2 If theyâ\x80\x99re getting too dark, place another pan under the baking sheet.
    \ ( 3 Doubling the pan will insulate the first baking sheet.) Bake for another
    \ 8 to 12 minutes, until the bagels are a golden brown. 4 13."
• source_sentence: causes for infection in the nerve of tooth sentences:
  • If a cavity is causing the toothache, your dentist will fill the cavity or possibly extract the tooth, if necessary. A root canal might be needed if the cause of the toothache is determined to be an infection of the tooth's nerve. Bacteria that have worked their way into the inner aspects of the tooth cause such an infection. An antibiotic may be prescribed if there is fever or swelling of the jaw.
  • "According to Article III, Section 1 of the Constitution, judges and justices
    \ of the Judicial Branch serve during good behavior.. This means they are appointed
    \ for life, unles â\x80¦ s they are impeached and removed from office. + 50 others
    \ found this useful.he term length for members of the House are two years and
    \ a staggering six years for members of the Senate."
  • Inflamed or infected pulp (pulpitis) most often causes a toothache. To relieve the pain and prevent further complications, the tooth may be extracted (surgically removed) or saved by root canal treatment.
• source_sentence: what county is hayden in sentences:
  • Normally, the Lead Agency is the agency with general governmental powers such as a city or a county. Agencies with limited powers or districts that provide a public service/utility such as a recreation and park district will tend to be a Responsible Agency.
  • According to the United States Census Bureau, the city has a total area of 9.61 square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01 square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake, and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is located on U.S. Route 95 at the junction of Route 41. It is also four miles (6 km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest of Hayden.
  • Hayden is a city in Kootenai County, Idaho, United States. Located in the northern portion of the state, just north of Coeur d'Alene, its population was 13,294 at the 2010 census. model-index:
• name: SentenceTransformer based on answerdotai/ModernBERT-base results:
  • task: type: triplet name: Triplet dataset: name: msmarco co condenser dev type: msmarco-co-condenser-dev metrics:
    • type: cosine_accuracy value: 0.984 name: Cosine Accuracy
  • dataset: config: en-ext name: MTEB AmazonCounterfactualClassification (en-ext) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics:
    • type: accuracy value: 65.997
    • type: f1 value: 53.595099999999995
    • type: f1_weighted value: 72.5304
    • type: ap value: 16.3093
    • type: ap_weighted value: 16.3093
    • type: main_score value: 65.997 task: type: Classification
  • dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics:
    • type: accuracy value: 64.19399999999999
    • type: f1 value: 58.5969
    • type: f1_weighted value: 67.7482
    • type: ap value: 28.0748
    • type: ap_weighted value: 28.0748
    • type: main_score value: 64.19399999999999 task: type: Classification
  • dataset: config: default name: MTEB AmazonPolarityClassification (default) revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics:
    • type: accuracy value: 67.1713
    • type: f1 value: 66.9443
    • type: f1_weighted value: 66.9443
    • type: ap value: 61.7296
    • type: ap_weighted value: 61.7296
    • type: main_score value: 67.1713 task: type: Classification
  • dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics:
    • type: accuracy value: 32.318000000000005
    • type: f1 value: 31.9973
    • type: f1_weighted value: 31.9973
    • type: main_score value: 32.318000000000005 task: type: Classification
  • dataset: config: default name: MTEB ArguAna (default) revision: c22ab2a51041ffd869aaddef7af8d8215647e41a split: test type: mteb/arguana metrics:
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    • type: precision_at_100 value: 0.962
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    • type: main_score value: 46.381 task: type: Retrieval
  • dataset: config: default name: MTEB ArxivClusteringP2P (default) revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics:
    • type: v_measure value: 38.9781
    • type: v_measure_std value: 14.0064
    • type: main_score value: 38.9781 task: type: Clustering
  • dataset: config: default name: MTEB ArxivClusteringS2S (default) revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics:
    • type: v_measure value: 28.964499999999997
    • type: v_measure_std value: 14.276900000000001
    • type: main_score value: 28.964499999999997 task: type: Clustering
  • dataset: config: default name: MTEB AskUbuntuDupQuestions (default) revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics:
    • type: map value: 57.9553
    • type: mrr value: 70.82929999999999
    • type: nAUC_map_max value: 23.2041
    • type: nAUC_map_std value: 21.0013
    • type: nAUC_map_diff1 value: 6.7763
    • type: nAUC_mrr_max value: 33.143499999999996
    • type: nAUC_mrr_std value: 25.8722
    • type: nAUC_mrr_diff1 value: 10.2834
    • type: main_score value: 57.9553 task: type: Reranking
  • dataset: config: default name: MTEB BIOSSES (default) revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics:
    • type: pearson value: 85.0909
    • type: spearman value: 80.7386
    • type: cosine_pearson value: 85.0909
    • type: cosine_spearman value: 80.7386
    • type: manhattan_pearson value: 82.0327
    • type: manhattan_spearman value: 78.562
    • type: euclidean_pearson value: 82.6159
    • type: euclidean_spearman value: 79.35419999999999
    • type: main_score value: 80.7386 task: type: STS
  • dataset: config: default name: MTEB Banking77Classification (default) revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics:
    • type: accuracy value: 82.026
    • type: f1 value: 81.9738
    • type: f1_weighted value: 81.9738
    • type: main_score value: 82.026 task: type: Classification
  • dataset: config: default name: MTEB BiorxivClusteringP2P (default) revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics:
    • type: v_measure value: 30.787
    • type: v_measure_std value: 0.9286000000000001
    • type: main_score value: 30.787 task: type: Clustering
  • dataset: config: default name: MTEB BiorxivClusteringS2S (default) revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics:
    • type: v_measure value: 27.099800000000002
    • type: v_measure_std value: 0.7908000000000001
    • type: main_score value: 27.099800000000002 task: type: Clustering
  • dataset: config: default name: MTEB CQADupstackAndroidRetrieval (default) revision: f46a197baaae43b4f621051089b82a364682dfeb split: test type: mteb/cqadupstack-android metrics:
    • type: ndcg_at_1 value: 29.757
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  • dataset: config: default name: MTEB CQADupstackGisRetrieval (default) revision: 5003b3064772da1887988e05400cf3806fe491f2 split: test type: mteb/cqadupstack-gis metrics:
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  • dataset: config: default name: MTEB CQADupstackTexRetrieval (default) revision: 46989137a86843e03a6195de44b09deda022eec7 split: test type: mteb/cqadupstack-tex metrics:
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  • dataset: config: default name: MTEB ImdbClassification (default) revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 split: test type: mteb/imdb metrics:
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  • dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics:
    • type: accuracy value: 88.9124
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  • dataset: config: en name: MTEB MTOPIntentClassification (en) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics:
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  • dataset: config: en name: MTEB MassiveIntentClassification (en) revision: 4672e20407010da34463acc759c162ca9734bca6 split: test type: mteb/amazon_massive_intent metrics:
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  • dataset: config: en name: MTEB MassiveScenarioClassification (en) revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 split: test type: mteb/amazon_massive_scenario metrics:
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  • dataset: config: default name: MTEB MedrxivClusteringP2P (default) revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 split: test type: mteb/medrxiv-clustering-p2p metrics:
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  • dataset: config: default name: MTEB MindSmallReranking (default) revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7 split: test type: mteb/mind_small metrics:
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  • dataset: config: default name: MTEB NFCorpus (default) revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 split: test type: mteb/nfcorpus metrics:
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  • dataset: config: default name: MTEB RedditClustering (default) revision: 24640382cdbf8abc73003fb0fa6d111a705499eb split: test type: mteb/reddit-clustering metrics:
    • type: v_measure value: 41.125699999999995
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  • dataset: config: default name: MTEB RedditClusteringP2P (default) revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 split: test type: mteb/reddit-clustering-p2p metrics:
    • type: v_measure value: 51.3872
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  • dataset: config: default name: MTEB SCIDOCS (default) revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 split: test type: mteb/scidocs metrics:
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  • dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics:
    • type: pearson value: 78.2689
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  • dataset: config: default name: MTEB STS12 (default) revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics:
    • type: pearson value: 73.3449
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    • type: cosine_pearson value: 73.3449
    • type: cosine_spearman value: 67.2036
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    • type: euclidean_pearson value: 68.4606
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    • type: main_score value: 67.2036 task: type: STS
  • dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics:
    • type: pearson value: 77.3137
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    • type: cosine_pearson value: 77.3137
    • type: cosine_spearman value: 77.4032
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  • dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics:
    • type: pearson value: 77.1255
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    • type: cosine_pearson value: 77.1255
    • type: cosine_spearman value: 72.3476
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    • type: manhattan_spearman value: 72.1268
    • type: euclidean_pearson value: 74.118
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  • dataset: config: default name: MTEB STS15 (default) revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics:
    • type: pearson value: 81.992
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    • type: cosine_pearson value: 81.992
    • type: cosine_spearman value: 82.4958
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    • type: manhattan_spearman value: 81.9727
    • type: euclidean_pearson value: 81.3543
    • type: euclidean_spearman value: 81.9557
    • type: main_score value: 82.4958 task: type: STS
  • dataset: config: default name: MTEB STS16 (default) revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics:
    • type: pearson value: 78.755
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    • type: cosine_pearson value: 78.755
    • type: cosine_spearman value: 79.408
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    • type: manhattan_spearman value: 80.5912
    • type: euclidean_pearson value: 80.0315
    • type: euclidean_spearman value: 80.5413
    • type: main_score value: 79.408 task: type: STS
  • dataset: config: fr-en name: MTEB STS17 (fr-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 41.280699999999996
    • type: spearman value: 39.9908
    • type: cosine_pearson value: 41.280699999999996
    • type: cosine_spearman value: 39.9908
    • type: manhattan_pearson value: 41.515
    • type: manhattan_spearman value: 39.6907
    • type: euclidean_pearson value: 41.5204
    • type: euclidean_spearman value: 39.6877
    • type: main_score value: 39.9908 task: type: STS
  • dataset: config: en-en name: MTEB STS17 (en-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 84.3554
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    • type: cosine_pearson value: 84.3554
    • type: cosine_spearman value: 84.7607
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    • type: euclidean_spearman value: 84.33070000000001
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  • dataset: config: es-en name: MTEB STS17 (es-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 36.045500000000004
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    • type: cosine_pearson value: 36.045500000000004
    • type: cosine_spearman value: 36.9017
    • type: manhattan_pearson value: 33.128099999999996
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  • dataset: config: en-de name: MTEB STS17 (en-de) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 38.332899999999995
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    • type: cosine_pearson value: 38.333
    • type: cosine_spearman value: 36.8844
    • type: manhattan_pearson value: 34.725699999999996
    • type: manhattan_spearman value: 34.0722
    • type: euclidean_pearson value: 35.0828
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    • type: main_score value: 36.8844 task: type: STS
  • dataset: config: en-tr name: MTEB STS17 (en-tr) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 8.5032
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    • type: cosine_pearson value: 8.5032
    • type: cosine_spearman value: 5.6236
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    • type: euclidean_pearson value: 11.6449
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    • type: main_score value: 5.6236 task: type: STS
  • dataset: config: it-en name: MTEB STS17 (it-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 30.772899999999996
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    • type: cosine_pearson value: 30.772899999999996
    • type: cosine_spearman value: 27.825899999999997
    • type: manhattan_pearson value: 34.1556
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    • type: euclidean_pearson value: 33.8333
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    • type: main_score value: 27.825899999999997 task: type: STS
  • dataset: config: nl-en name: MTEB STS17 (nl-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 37.4576
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    • type: cosine_spearman value: 33.5406
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    • type: manhattan_spearman value: 36.8791
    • type: euclidean_pearson value: 39.4756
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  • dataset: config: en-ar name: MTEB STS17 (en-ar) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 12.8259
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    • type: cosine_pearson value: 12.8259
    • type: cosine_spearman value: 13.2374
    • type: manhattan_pearson value: 16.0609
    • type: manhattan_spearman value: 14.8943
    • type: euclidean_pearson value: 14.1566
    • type: euclidean_spearman value: 12.6913
    • type: main_score value: 13.2374 task: type: STS
  • dataset: config: en name: MTEB STS22 (en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics:
    • type: pearson value: 66.1988
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    • type: cosine_pearson value: 66.1988
    • type: cosine_spearman value: 66.6842
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    • type: manhattan_spearman value: 66.00200000000001
    • type: euclidean_pearson value: 66.9588
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    • type: main_score value: 66.6842 task: type: STS
  • dataset: config: es-en name: MTEB STS22 (es-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics:
    • type: pearson value: 55.525999999999996
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    • type: cosine_pearson value: 55.525999999999996
    • type: cosine_spearman value: 58.790600000000005
    • type: manhattan_pearson value: 57.918499999999995
    • type: manhattan_spearman value: 60.4479
    • type: euclidean_pearson value: 58.0086
    • type: euclidean_spearman value: 60.5008
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  • dataset: config: zh-en name: MTEB STS22 (zh-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics:
    • type: pearson value: 36.3507
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    • type: cosine_pearson value: 36.3506
    • type: cosine_spearman value: 39.9716
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    • type: manhattan_spearman value: 28.190700000000003
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    • type: main_score value: 39.9716 task: type: STS
  • dataset: config: de-en name: MTEB STS22 (de-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics:
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    • type: cosine_pearson value: 47.759800000000006
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  • dataset: config: pl-en name: MTEB STS22 (pl-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics:
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  • dataset: config: default name: MTEB STSBenchmark (default) revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics:
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  • dataset: config: default name: MTEB SciDocsRR (default) revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab split: test type: mteb/scidocs-reranking metrics:
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  • dataset: config: default name: MTEB SciFact (default) revision: 0228b52cf27578f30900b9e5271d331663a030d7 split: test type: mteb/scifact metrics:
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    • type: map_at_1000 value: 56.397
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  • dataset: config: default name: MTEB SprintDuplicateQuestions (default) revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics:
    • type: similarity_accuracy value: 99.802
    • type: similarity_accuracy_threshold value: 68.9054
    • type: similarity_f1 value: 89.89439999999999
    • type: similarity_f1_threshold value: 68.1838
    • type: similarity_precision value: 90.3943
    • type: similarity_recall value: 89.4
    • type: similarity_ap value: 95.1114
    • type: cosine_accuracy value: 99.802
    • type: cosine_accuracy_threshold value: 68.9053
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    • type: cosine_precision value: 90.3943
    • type: cosine_recall value: 89.4
    • type: cosine_ap value: 95.1114
    • type: manhattan_accuracy value: 99.8
    • type: manhattan_accuracy_threshold value: 56569.586200000005
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    • type: manhattan_f1_threshold value: 56569.586200000005
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    • type: manhattan_recall value: 89.8
    • type: manhattan_ap value: 94.9934
    • type: euclidean_accuracy value: 99.799
    • type: euclidean_accuracy_threshold value: 2539.7804
    • type: euclidean_f1 value: 89.8348
    • type: euclidean_f1_threshold value: 2568.8804999999998
    • type: euclidean_precision value: 89.9699
    • type: euclidean_recall value: 89.7
    • type: euclidean_ap value: 95.06
    • type: dot_accuracy value: 99.7723
    • type: dot_accuracy_threshold value: 72398.77320000001
    • type: dot_f1 value: 88.0829
    • type: dot_f1_threshold value: 72398.77320000001
    • type: dot_precision value: 91.39779999999999
    • type: dot_recall value: 85.0
    • type: dot_ap value: 93.74040000000001
    • type: max_accuracy value: 99.802
    • type: max_f1 value: 89.89439999999999
    • type: max_precision value: 91.39779999999999
    • type: max_recall value: 89.8
    • type: max_ap value: 95.1114
    • type: main_score value: 95.1114 task: type: PairClassification
  • dataset: config: default name: MTEB StackExchangeClustering (default) revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics:
    • type: v_measure value: 56.641
    • type: v_measure_std value: 4.6061
    • type: main_score value: 56.641 task: type: Clustering
  • dataset: config: default name: MTEB StackExchangeClusteringP2P (default) revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics:
    • type: v_measure value: 31.850099999999998
    • type: v_measure_std value: 1.3043
    • type: main_score value: 31.850099999999998 task: type: Clustering
  • dataset: config: default name: MTEB StackOverflowDupQuestions (default) revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 split: test type: mteb/stackoverflowdupquestions-reranking metrics:
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    • type: mrr value: 48.8603
    • type: nAUC_map_max value: 14.3434
    • type: nAUC_map_std value: 8.115400000000001
    • type: nAUC_map_diff1 value: 35.017700000000005
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    • type: main_score value: 48.149300000000004 task: type: Reranking
  • dataset: config: default name: MTEB SummEval (default) revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics:
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    • type: dot_spearman value: 29.713099999999997
    • type: dot_pearson value: 28.3169
    • type: main_score value: 31.8365 task: type: Summarization
  • dataset: config: default name: MTEB TRECCOVID (default) revision: bb9466bac8153a0349341eb1b22e06409e78ef4e split: test type: mteb/trec-covid metrics:
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  • dataset: config: default name: MTEB ToxicConversationsClassification (default) revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de split: test type: mteb/toxic_conversations_50k metrics:
    • type: accuracy value: 64.41409999999999
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    • type: f1_weighted value: 72.2769
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  • dataset: config: default name: MTEB TweetSentimentExtractionClassification (default) revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics:
    • type: accuracy value: 52.6316
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    • type: main_score value: 52.6316 task: type: Classification
  • dataset: config: default name: MTEB TwentyNewsgroupsClustering (default) revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics:
    • type: v_measure value: 34.3169
    • type: v_measure_std value: 1.669
    • type: main_score value: 34.3169 task: type: Clustering
  • dataset: config: default name: MTEB TwitterSemEval2015 (default) revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics:
    • type: similarity_accuracy value: 83.45949999999999
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    • type: max_precision value: 57.6589
    • type: max_recall value: 68.628
    • type: max_ap value: 65.0861
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  • dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics:
    • type: similarity_accuracy value: 88.1612
    • type: similarity_accuracy_threshold value: 63.8545
    • type: similarity_f1 value: 76.3737
    • type: similarity_f1_threshold value: 60.4226
    • type: similarity_precision value: 74.1711
    • type: similarity_recall value: 78.7111
    • type: similarity_ap value: 83.5651
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SentenceTransformer based on answerdotai/ModernBERT-base

This is a sentence-transformers model finetuned from answerdotai/ModernBERT-base on the msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

I finetune ModernBERT-base using script from offical repo train_st.py on a RTX 4090 GPU with the only change of setting mini-batch size of CachedMultipleNegativesRankingLoss to 128. Training for 1 epoch takes less than an hour.

The mini-batch size of GradCache should not change model performnace, but the finetuned model performs better than that recorded in the paper. See MTEB results in the result folder mteb.

Training logs can be found here: https://api.wandb.ai/links/joe32140/ekuauaao.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: answerdotai/ModernBERT-base
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("joe32140/ModernBERT-base-msmarco")
# Run inference
sentences = [
    'what county is hayden in',
    "Hayden is a city in Kootenai County, Idaho, United States. Located in the northern portion of the state, just north of Coeur d'Alene, its population was 13,294 at the 2010 census.",
    "According to the United States Census Bureau, the city has a total area of 9.61 square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01 square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake, and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is located on U.S. Route 95 at the junction of Route 41. It is also four miles (6 km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest of Hayden.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

• Dataset: msmarco-co-condenser-dev
• Evaluated with TripletEvaluator

Training Details

Training Dataset

msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1

• Dataset: msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 at 84ed2d3
• Size: 11,662,655 training samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 9.26 tokens max: 34 tokens	min: 17 tokens mean: 79.14 tokens max: 222 tokens	min: 24 tokens mean: 80.09 tokens max: 436 tokens

• Samples:

  query	positive	negative
  what is the meaning of menu planning	Menu planning is the selection of a menu for an event. Such as picking out the dinner for your wedding or even a meal at a Birthday Party. Menu planning is when you are preparing a calendar of meals and you have to sit down and decide what meat and veggies you want to serve on each certain day.	Menu Costs. In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general.
  how old is brett butler	Brett Butler is 59 years old. To be more precise (and nerdy), the current age as of right now is 21564 days or (even more geeky) 517536 hours. That's a lot of hours!	Passed in: St. John's, Newfoundland and Labrador, Canada. Passed on: 16/07/2016. Published in the St. John's Telegram. Passed away suddenly at the Health Sciences Centre surrounded by his loving family, on July 16, 2016 Robert (Bobby) Joseph Butler, age 52 years. Predeceased by his special aunt Geri Murrin and uncle Mike Mchugh; grandparents Joe and Margaret Murrin and Jack and Theresa Butler.
  when was the last navajo treaty sign?	In Executive Session, Senate of the United States, July 25, 1868. Resolved, (two-thirds of the senators present concurring,) That the Senate advise and consent to the ratification of the treaty between the United States and the Navajo Indians, concluded at Fort Sumner, New Mexico, on the first day of June, 1868.	Share Treaty of Greenville. The Treaty of Greenville was signed August 3, 1795, between the United States, represented by Gen. Anthony Wayne, and chiefs of the Indian tribes located in the Northwest Territory, including the Wyandots, Delawares, Shawnees, Ottawas, Miamis, and others.

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1

• Dataset: msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 at 84ed2d3
• Size: 11,662,655 evaluation samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 9.2 tokens max: 27 tokens	min: 21 tokens mean: 80.44 tokens max: 241 tokens	min: 23 tokens mean: 80.38 tokens max: 239 tokens

• Samples:

  query	positive	negative
  what county is holly springs nc in	Holly Springs, North Carolina. Holly Springs is a town in Wake County, North Carolina, United States. As of the 2010 census, the town population was 24,661, over 2½ times its population in 2000. Contents.	The Mt. Holly Springs Park & Resort. One of the numerous trolley routes that carried people around the county at the turn of the century was the Carlisle & Mt. Holly Railway Company. The “Holly Trolley” as it came to be known was put into service by Patricio Russo and made its first run on May 14, 1901.
  how long does nyquil stay in your system	In order to understand exactly how long Nyquil lasts, it is absolutely vital to learn about the various ingredients in the drug. One of the ingredients found in Nyquil is Doxylamine, which is an antihistamine. This specific medication has a biological half-life or 6 to 12 hours. With this in mind, it is possible for the drug to remain in the system for a period of 12 to 24 hours. It should be known that the specifics will depend on a wide variety of different factors, including your age and metabolism.	I confirmed that NyQuil is about 10% alcohol, a higher content than most domestic beers. When I asked about the relatively high proof, I was told that the alcohol dilutes the active ingredients. The alcohol free version is there for customers with addiction issues.. also found that in that version there is twice the amount of DXM. When I asked if I could speak to a chemist or scientist, I was told they didn't have anyone who fit that description there. It’s been eight years since I kicked NyQuil. I've been sober from alcohol for four years.
  what are mineral water	1 Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source. Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2 It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source.	Minerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.inerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• learning_rate: 8e-05
• num_train_epochs: 1
• warmup_ratio: 0.05
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.9
• Sentence Transformers: 3.3.1
• Transformers: 4.48.0.dev0
• PyTorch: 2.4.0
• Accelerate: 1.2.1
• Datasets: 2.21.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}