diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1407 @@ +--- +datasets: +- cfli/bge-full-data +library_name: sentence-transformers +metrics: +- cosine_accuracy@1 +- cosine_accuracy@3 +- cosine_accuracy@5 +- cosine_accuracy@10 +- cosine_precision@1 +- cosine_precision@3 +- cosine_precision@5 +- cosine_precision@10 +- cosine_recall@1 +- cosine_recall@3 +- cosine_recall@5 +- cosine_recall@10 +- cosine_ndcg@10 +- cosine_mrr@10 +- cosine_map@100 +pipeline_tag: sentence-similarity +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:1770649 +- loss:CachedMultipleNegativesRankingLoss +widget: +- source_sentence: what is the pulse in your wrist called + sentences: + - 'Pulse cm up the forearm is suggestive of arteriosclerosis. In coarctation of + aorta, femoral pulse may be significantly delayed as compared to radial pulse + (unless there is coexisting aortic regurgitation). The delay can also be observed + in supravalvar aortic stenosis. Several pulse patterns can be of clinically significance. + These include: Chinese medicine has focused on the pulse in the upper limbs for + several centuries. The concept of pulse diagnosis is essentially based on palpation + and observations of the radial and ulnar volar pulses at the readily accessible + wrist. Although the pulse can be felt in multiple places in the head, people' + - Pulse diagnosis into three positions on each wrist. The first pulse closest to + the wrist is the "cun" (inch, 寸) position, the second "guan" (gate, 關), and the + third pulse position furthest away from the wrist is the "chi" (foot, 尺). There + are several systems of diagnostic interpretation of pulse findings utilised in + the Chinese medicine system. Some systems (Cun Kou) utilise overall pulse qualities, + looking at changes in the assessed parameters of the pulse to derive one of the + traditional 28 pulse types. Other approaches focus on individual pulse positions, + looking at changes in the pulse quality and strength within the + - 'Pre-hospital trauma assessment inside of the wrist toward the thumb. For unresponsive + adult patients, checking pulse is performed by palpating the carotid artery in + the neck. For infants and small children, the pulse is usually assessed in the + brachial artery in the upper arm. After confirming that the pulse is present, + the final step in the initial assessment for a trauma patient is to check for + any gross bleeding and to control it. Should a pulse not be detected, or in the + case of a child or infant is present but at a rate less than 60, cardiovascular + resuscitation will be commenced. Steps:' + - Pulse Pulse In medicine, a pulse represents the tactile arterial palpation of + the heartbeat by trained fingertips. The pulse may be palpated in any place that + allows an artery to be compressed near the surface of the body, such as at the + neck (carotid artery), wrist (radial artery), at the groin (femoral artery), behind + the knee (popliteal artery), near the ankle joint (posterior tibial artery), and + on foot (dorsalis pedis artery). Pulse (or the count of arterial pulse per minute) + is equivalent to measuring the heart rate. The heart rate can also be measured + by listening to the heart beat by + - Pulse diagnosis dosha. The middle finger and ring finger are placed next to the + index finger and represents consequently the Pitta and Kapha doshas of the patient. + Pulse can be measured in the superficial, middle, and deep levels thus obtaining + more information regarding energy imbalance of the patient. The main sites for + pulse assessment are the radial arteries in the left and right wrists, where it + overlays the styloid process of the radius, between the wrist crease and extending + proximal, approximately 5 cm in length (or 1.9 cun, where the forearm is 12 cun). + In traditional Chinese medicine, the pulse is divided + - 'Pulse auscultation, traditionally using a stethoscope and counting it for a minute. + The radial pulse is commonly measured using three fingers. This has a reason: + the finger closest to the heart is used to occlude the pulse pressure, the middle + finger is used get a crude estimate of the blood pressure, and the finger most + distal to the heart (usually the ring finger) is used to nullify the effect of + the ulnar pulse as the two arteries are connected via the palmar arches (superficial + and deep). The study of the pulse is known as sphygmology. Claudius Galen was + perhaps the first' +- source_sentence: Diet and Mass Conservation--We weigh as much as we eat? + sentences: + - '[This thread](_URL_0_) contains a good comment string based on /u/Redwing999 + experience and some written sources on insect obesity.' + - We have two chemicals. One that tells us that we're full and the other that tells + us something gives us pleasure. Through evolution, they made sure that the balance + wouldn't tip. Now, the latter can override the former. That means you eat cake + because it gives you pleasure even though you're full as hell. The balance has + tipped and temptation gets in our way. This is one of the reasons for obesity! + - This question actually has nothing to do with the law of conservation of mass + or energy. You don't take up more mass by exercising; in fact, you technically + **lose** mass because you are sweating water and other substances out, as well + as converting your food into heat and having this heat escape your body. It's + just that when your muscle fibers are damaged through exercise, they "over-heal" + (to put it very unsophisticated-sounding). The food you eat contributes to feeding + these growing muscles, which adds more mass to your body. So you *lose* mass through + exercising, but more than make up for it with a proper diet. + - A professor of nutrition went on a diet for 10 weeks, consisting largely of twinkies, + oreos, and doritos. While still maintaining multivitamins and a protein shake + daily with occasional greens as well to not go completely off the deep end. After + the 10 weeks of controlling a steady stream of 1,800 calories a day he lost 27 + pounds, lowered his bad cholesterol by 20% and upping his good cholesterol also + by 20%. Most weight loss is from a steady intake in a caloric deficit (IE don't + eat 1,700 of your daily 1,800 in one meal). If you do this make sure to also grab + multivitamins if you don't already have them, and ensure you're getting some protein. + Obviously these are also just short term results, and it's not recommended you + over indulge in junk food over a balanced diet and daily exercise. Article link + here (sorry for ghetto link I'm on my phone) _URL_0_ + - This is a great question. I hope we get some real answers. I don't chew my food + much, I'm pretty skinny and eat a ton..I always wondered if chewing less makes + less nutrients available for absorption + - There is a tremendous amount of misinformation surrounding calories and weight. + [This blog entry](_URL_0_) does a good job of presenting why people so often get + confused with regards to thermodynamics and food. There's a lot to learn, but + it's a good start. +- source_sentence: Are Jett Pangan and Jon Fratelli both from Scotland? + sentences: + - Gary Lightbody Gary Lightbody (born 15 June 1976) is a Northern Irish singer, + songwriter, guitarist and multi-instrumentalist, best known as the lead singer + and rhythm guitarist of the Northern Irish-Scottish rock band Snow Patrol. + - Ray Wilson (musician) Raymond Wilson (born 8 September 1968) is a Scottish musician, + best known as vocalist in the post-grunge band Stiltskin, and in Genesis from + 1996 to 1998. + - Peter Frampton Peter Kenneth Frampton (born 22 April 1950) is an English rock + musician, singer, songwriter, producer, and guitarist. He was previously associated + with the bands Humble Pie and The Herd. At the end of his 'group' career was Frampton's + international breakthrough album his live release, "Frampton Comes Alive!" The + album sold in the United States more than 8 million copies and spawned several + single hits. Since then he has released several major albums. He has also worked + with David Bowie and both Matt Cameron and Mike McCready from Pearl Jam, among + others. + - Rob Wainwright (rugby union) Robert Iain Wainwright (born 22 March 1965 in Perth, + Scotland) is a former rugby union footballer who was capped 37 times for Scotland + (Captain 16 times) and once for the British and Irish Lions. He played flanker. + - Bert Jansch Herbert "Bert" Jansch (3 November 1943 – 5 October 2011) was a Scottish + folk musician and founding member of the band Pentangle. He was born in Glasgow + and came to prominence in London in the 1960s, as an acoustic guitarist, as well + as a singer-songwriter. He recorded at least 25 albums and toured extensively + from the 1960s to the 21st century. + - Jett Pangan Jett Pangan (born Reginald Pangan on June 21, 1968) is a Filipino + singer and guitarist best known for fronting the Filipino rock bands The Dawn, + and the now defunct Jett Pangan Group. He is also an actor, appearing in several + TV and films, most notably his role in "Tulad ng Dati". He is the half-brother + of John Lapus. +- source_sentence: How can I control my mind from thinking too much? + sentences: + - Why is it that we always think about anything too much which is not even worth + thinking? + - When I'm around people I love my mind goes blank. As I get closer to someone it + gets worse and worse. How can I change my way of thinking? + - Why am I thinking too much? + - Why am I thinking too much about everything? + - If I keep choosing not to fully think about a concept or grab onto it when it + appears in my mind while I am reading or doing something else, am I damaging my + brain's ability to understand and act on those things in the future? + - How do I keep my mind from thinking too much over a thing? +- source_sentence: Who won 23 World Rally Championships, two in particular with the + Lancia Delta Group A rally car? + sentences: + - Lancia Delta Group A The Lancia Delta Group A is a Group A rally car built for + the Martini Lancia by Lancia to compete in the World Rally Championship. It is + based upon the Lancia Delta road car and replaced the Lancia Delta S4. The car + was introduced for the 1987 World Rally Championship season and dominated the + World Rally Championship, scoring 46 WRC victories overall and winning the constructors' + championship a record six times in a row from 1987 to 1992, in addition to drivers' + championship titles for Juha Kankkunen (1987 and 1991) and Miki Biasion (1988 + and 1989), making Lancia the most successful marque in the history of the WRC + and the Delta the most successful car. + - Luis Moya Luis Rodríguez Moya, better known as Luis Moya (born 23 September 1960 + in La Coruña, Spain) is a now-retired rally co-driver, synonymous with driver + Carlos Sainz. He is the third most successful co-driver in the history of the + World Rally Championship (WRC), after Daniel Elena and Timo Rautiainen + - 2016 World Rally Championship-3 The 2016 World Rally Championship-3 was the fourth + season of the World Rally Championship-3, an auto racing championship recognized + by the Fédération Internationale de l'Automobile, ran in support of the World + Rally Championship. It was created when the Group R class of rally car was introduced + in 2013. The Championship was composed of fourteen rallies, and drivers and teams + had to nominate a maximum of six events. The best five results counted towards + the championship. + - 2015 Rally Catalunya The 2015 Rally Catalunya (formally the 51º Rally RACC Catalunya + – Costa Daurada) was the twelfth round of the 2015 World Rally Championship. The + race was held over four days between 22 October and 25 October 2015, and operated + out of Salou, Catalonia, Spain. Volkswagen's Andreas Mikkelsen won the race, his + first win in the World Rally Championship. + - 'Lancia Rally 037 The Lancia Rally ("Tipo 151", also known as the Lancia Rally + 037, Lancia 037 or Lancia-Abarth #037 from its Abarth project code "037") was + a mid-engine sports car and rally car built by Lancia in the early 1980s to compete + in the FIA Group B World Rally Championship. Driven by Markku Alén, Attilio Bettega, + and Walter Röhrl, the car won Lancia the manufacturers'' world championship in + the 1983 season. It was the last rear-wheel drive car to win the WRC.' + - John Lund (racing driver) John Lund (born 12 January 1954) is a BriSCA Formula + 1 Stock Cars racing driver from Rimington, Lancashire who races under number 53. + Lund is one of the most successful stock car drivers of all time and holds the + current record for the most World Championship wins. +model-index: +- name: SentenceTransformer + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoClimateFEVER + type: NanoClimateFEVER + metrics: + - type: cosine_accuracy@1 + value: 0.22 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.52 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.6 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.64 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.22 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.20666666666666667 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.14400000000000002 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.084 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.08833333333333332 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.26666666666666666 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.30833333333333335 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.35666666666666663 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.2839842522559327 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.37471428571428567 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.2232144898031751 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoDBPedia + type: NanoDBPedia + metrics: + - type: cosine_accuracy@1 + value: 0.7 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.74 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.8 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.86 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.48 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.43200000000000005 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.3760000000000001 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.07263002775640012 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.11337585016033845 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.15857516982468162 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.23454122344078535 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.4732884231947513 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.738888888888889 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.334802367685341 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoFEVER + type: NanoFEVER + metrics: + - type: cosine_accuracy@1 + value: 0.88 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.96 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 1.0 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 1.0 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.88 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.33333333333333326 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.20799999999999996 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.10799999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.8266666666666667 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.9233333333333333 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.9533333333333333 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.9733333333333333 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.920250305861268 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.9266666666666665 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.8908062417949636 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoFiQA2018 + type: NanoFiQA2018 + metrics: + - type: cosine_accuracy@1 + value: 0.46 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.62 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.68 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.74 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.46 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.2866666666666667 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.22399999999999995 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.13399999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.24452380952380953 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.4037936507936508 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.4890396825396825 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.5964206349206349 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.49008883369308526 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.5513333333333333 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.4201188803513742 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoHotpotQA + type: NanoHotpotQA + metrics: + - type: cosine_accuracy@1 + value: 0.82 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.94 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.94 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.96 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.82 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.38666666666666655 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.24799999999999997 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.132 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.41 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.58 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.62 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.66 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.6699619900438456 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8795238095238095 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.5983592359151276 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoMSMARCO + type: NanoMSMARCO + metrics: + - type: cosine_accuracy@1 + value: 0.34 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.6 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.72 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.82 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.34 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.2 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.14400000000000002 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.08199999999999999 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.34 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.72 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.82 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.5747097116234108 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.4967380952380951 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.5049567742199321 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoNFCorpus + type: NanoNFCorpus + metrics: + - type: cosine_accuracy@1 + value: 0.36 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.5 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.56 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.62 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.36 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.2933333333333333 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.296 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.22 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.015576651798182985 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.03488791186499473 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.06408574388859087 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.07971201227506045 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.25470834876894616 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.4443888888888889 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.09234660597563751 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoNQ + type: NanoNQ + metrics: + - type: cosine_accuracy@1 + value: 0.46 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.66 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.7 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.78 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.46 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.22 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.14400000000000002 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.08399999999999999 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.45 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.61 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.66 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.75 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.6060972125930784 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.569079365079365 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.5645161933196003 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoQuoraRetrieval + type: NanoQuoraRetrieval + metrics: + - type: cosine_accuracy@1 + value: 0.94 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.98 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.98 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 1.0 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.94 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.40666666666666657 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.25199999999999995 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.13599999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.8173333333333332 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.9453333333333334 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.956 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.9933333333333334 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.9593808852823181 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.9625 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.9422896825396825 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoSCIDOCS + type: NanoSCIDOCS + metrics: + - type: cosine_accuracy@1 + value: 0.48 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.66 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.74 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.86 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.48 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.33333333333333326 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.276 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.20199999999999996 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.10166666666666668 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.20666666666666664 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.2846666666666667 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.41566666666666663 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.3972031938693105 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.5927698412698412 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.304253910983743 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoArguAna + type: NanoArguAna + metrics: + - type: cosine_accuracy@1 + value: 0.26 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.64 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.8 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.26 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.21333333333333335 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.16 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.08999999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.26 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.64 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.8 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.9 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.5855962294470597 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.48385714285714276 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.48932444805879344 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoSciFact + type: NanoSciFact + metrics: + - type: cosine_accuracy@1 + value: 0.34 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.48 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.54 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.6 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.34 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.18 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.128 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.07 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.305 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.47 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.54 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.6 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.45719389021878065 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.4177460317460317 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.41560718364765603 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoTouche2020 + type: NanoTouche2020 + metrics: + - type: cosine_accuracy@1 + value: 0.4897959183673469 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.8367346938775511 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.8979591836734694 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9795918367346939 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.4897959183673469 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.5034013605442177 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.4653061224489797 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.36122448979591837 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.03552902483256089 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.10751588484963115 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.16516486949441941 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.24301991055992778 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.4179864214131331 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.6742306446388079 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.30799309847167516 + name: Cosine Map@100 + - task: + type: nano-beir + name: Nano BEIR + dataset: + name: NanoBEIR mean + type: NanoBEIR_mean + metrics: + - type: cosine_accuracy@1 + value: 0.519215070643642 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.7028257456828885 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.7659968602825747 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.8276609105180532 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.519215070643642 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.31103087388801676 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.2401004709576139 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.1599403453689168 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.30517380876238104 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.4539671767437396 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.5168614460831313 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.5863610600920313 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.5454192075588399 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.6240336149111658 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.4683530086743616 + name: Cosine Map@100 +--- + +# SentenceTransformer + +This is a [sentence-transformers](https://www.SBERT.net) model trained on the [bge-full-data](https://huggingface.co/datasets/cfli/bge-full-data) 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. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer + +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 dimensions +- **Similarity Function:** Cosine Similarity +- **Training Dataset:** + - [bge-full-data](https://huggingface.co/datasets/cfli/bge-full-data) + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, '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: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("NohTow/ModernBERT-base-DPR-fullneg-gte-0.0002") +# Run inference +sentences = [ + 'Who won 23 World Rally Championships, two in particular with the Lancia Delta Group A rally car?', + "Lancia Delta Group A The Lancia Delta Group A is a Group A rally car built for the Martini Lancia by Lancia to compete in the World Rally Championship. It is based upon the Lancia Delta road car and replaced the Lancia Delta S4. The car was introduced for the 1987 World Rally Championship season and dominated the World Rally Championship, scoring 46 WRC victories overall and winning the constructors' championship a record six times in a row from 1987 to 1992, in addition to drivers' championship titles for Juha Kankkunen (1987 and 1991) and Miki Biasion (1988 and 1989), making Lancia the most successful marque in the history of the WRC and the Delta the most successful car.", + 'Luis Moya Luis Rodríguez Moya, better known as Luis Moya (born 23 September 1960 in La Coruña, Spain) is a now-retired rally co-driver, synonymous with driver Carlos Sainz. He is the third most successful co-driver in the history of the World Rally Championship (WRC), after Daniel Elena and Timo Rautiainen', +] +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 + +#### Information Retrieval + +* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` +* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) + +| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | +|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| +| cosine_accuracy@1 | 0.22 | 0.7 | 0.88 | 0.46 | 0.82 | 0.34 | 0.36 | 0.46 | 0.94 | 0.48 | 0.26 | 0.34 | 0.4898 | +| cosine_accuracy@3 | 0.52 | 0.74 | 0.96 | 0.62 | 0.94 | 0.6 | 0.5 | 0.66 | 0.98 | 0.66 | 0.64 | 0.48 | 0.8367 | +| cosine_accuracy@5 | 0.6 | 0.8 | 1.0 | 0.68 | 0.94 | 0.72 | 0.56 | 0.7 | 0.98 | 0.74 | 0.8 | 0.54 | 0.898 | +| cosine_accuracy@10 | 0.64 | 0.86 | 1.0 | 0.74 | 0.96 | 0.82 | 0.62 | 0.78 | 1.0 | 0.86 | 0.9 | 0.6 | 0.9796 | +| cosine_precision@1 | 0.22 | 0.7 | 0.88 | 0.46 | 0.82 | 0.34 | 0.36 | 0.46 | 0.94 | 0.48 | 0.26 | 0.34 | 0.4898 | +| cosine_precision@3 | 0.2067 | 0.48 | 0.3333 | 0.2867 | 0.3867 | 0.2 | 0.2933 | 0.22 | 0.4067 | 0.3333 | 0.2133 | 0.18 | 0.5034 | +| cosine_precision@5 | 0.144 | 0.432 | 0.208 | 0.224 | 0.248 | 0.144 | 0.296 | 0.144 | 0.252 | 0.276 | 0.16 | 0.128 | 0.4653 | +| cosine_precision@10 | 0.084 | 0.376 | 0.108 | 0.134 | 0.132 | 0.082 | 0.22 | 0.084 | 0.136 | 0.202 | 0.09 | 0.07 | 0.3612 | +| cosine_recall@1 | 0.0883 | 0.0726 | 0.8267 | 0.2445 | 0.41 | 0.34 | 0.0156 | 0.45 | 0.8173 | 0.1017 | 0.26 | 0.305 | 0.0355 | +| cosine_recall@3 | 0.2667 | 0.1134 | 0.9233 | 0.4038 | 0.58 | 0.6 | 0.0349 | 0.61 | 0.9453 | 0.2067 | 0.64 | 0.47 | 0.1075 | +| cosine_recall@5 | 0.3083 | 0.1586 | 0.9533 | 0.489 | 0.62 | 0.72 | 0.0641 | 0.66 | 0.956 | 0.2847 | 0.8 | 0.54 | 0.1652 | +| cosine_recall@10 | 0.3567 | 0.2345 | 0.9733 | 0.5964 | 0.66 | 0.82 | 0.0797 | 0.75 | 0.9933 | 0.4157 | 0.9 | 0.6 | 0.243 | +| **cosine_ndcg@10** | **0.284** | **0.4733** | **0.9203** | **0.4901** | **0.67** | **0.5747** | **0.2547** | **0.6061** | **0.9594** | **0.3972** | **0.5856** | **0.4572** | **0.418** | +| cosine_mrr@10 | 0.3747 | 0.7389 | 0.9267 | 0.5513 | 0.8795 | 0.4967 | 0.4444 | 0.5691 | 0.9625 | 0.5928 | 0.4839 | 0.4177 | 0.6742 | +| cosine_map@100 | 0.2232 | 0.3348 | 0.8908 | 0.4201 | 0.5984 | 0.505 | 0.0923 | 0.5645 | 0.9423 | 0.3043 | 0.4893 | 0.4156 | 0.308 | + +#### Nano BEIR + +* Dataset: `NanoBEIR_mean` +* Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) + +| Metric | Value | +|:--------------------|:-----------| +| cosine_accuracy@1 | 0.5192 | +| cosine_accuracy@3 | 0.7028 | +| cosine_accuracy@5 | 0.766 | +| cosine_accuracy@10 | 0.8277 | +| cosine_precision@1 | 0.5192 | +| cosine_precision@3 | 0.311 | +| cosine_precision@5 | 0.2401 | +| cosine_precision@10 | 0.1599 | +| cosine_recall@1 | 0.3052 | +| cosine_recall@3 | 0.454 | +| cosine_recall@5 | 0.5169 | +| cosine_recall@10 | 0.5864 | +| **cosine_ndcg@10** | **0.5454** | +| cosine_mrr@10 | 0.624 | +| cosine_map@100 | 0.4684 | + + + + + +## Training Details + +### Training Dataset + +#### bge-full-data + +* Dataset: [bge-full-data](https://huggingface.co/datasets/cfli/bge-full-data) at [78f5c99](https://huggingface.co/datasets/cfli/bge-full-data/tree/78f5c99b534a52824ab26bd24edda592eaed4c7a) +* Size: 1,770,649 training samples +* Columns: anchor, positive, negative_0, negative_1, negative_2, negative_3, and negative_4 +* Approximate statistics based on the first 1000 samples: + | | anchor | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | + |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| + | type | string | string | string | string | string | string | string | + | details | | | | | | | | +* Samples: + | anchor | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 | + |:-----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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+ | What happens if you eat raw chicken? | What are the dangers of eating raw chicken? | Does all raw chicken have salmonella? | How safe is to eat chicken during pregnancy? | What meats are safe to eat raw? | What are some natural obligations of chicken? | Is it safe to eat raw egg? | + | how long does it take for a wren egg to hatch | How often does a mother Wren sit on her nest? I don't know for sure about how long Wrens usually spend on the nest at one sitting.. (Sorry couldn't resist the joke) However, the eggs usually hatch in 13-18 days, so if there were no hatchlings when that time elapsed, then you'd know for sure that she hadn't been behaving normally. | - When you are trying to hatch Tennessee red quail eggs, it will take approximately 23 days. You should perform lock down on the egg at 20 days. This is a period of time whe … n there should be no disturbances because hatching is likely to begin.urkey eggs usually take 21 to 28 days to hatch depending on what they are incubated in like an incubator or by a hen. | How long does it take an egg to hatch? For an average Eagle it would have a time for about 32-36 days, but the average time for an Eagle egg to hatch is about 35 days. 28 people found this useful. | - When you are trying to hatch Tennessee red quail eggs, it will take approximately 23 days. You should perform lock down on the egg at 20 days. This is a period of time whe … n there should be no disturbances because hatching is likely to begin.urkey eggs usually take 21 to 28 days to hatch depending on what they are incubated in like an incubator or by a hen. It also depends on how fertile it is and how it is cared … for. | - Actually this may vary depending on the kind of bird finch, the eggs hatch in between 12 - 16 days or 3 weeks.The nestlings fledge in 18 - 19 days.ctually this may vary depending on the kind of bird finch, the eggs hatch in between 12 - 16 days or 3 weeks. | - Welcome, and thanks for visiting the virtual home of the Whitestown Fire Department. Whether you’re stopping by to obtain information on our department, place a comment, track our progress and events, or just looking at the great pictures of our top notch personnel in action, we hope that you find what you’re after. Please feel free to provide feedback or contact us for any questions you may have. | + | can you have schizophrenia and bipolar | Can you have both bipolar disorder and schizophrenia? Health Mental Health Can you have both bipolar disorder and schizophrenia? I'm 19 and was diagnosed with Bipolar Disorder almost 2 years ago. I also have some symptoms of schizophrenia such as auditory hallucinations and occasional visual ones as well and occasional paranoia. Ok the paranoia is pretty frequent. So yea, Can you have both of them? I know some of the symptoms can be... show more Follow 6 answers Answers Relevance Rating Newest Oldest Best Answer: yes you can, but some people with bipolar disorder have hallucinations and delusions from the bipolar disorder. only a psychiatrist could diagnose you i guess. Source (s):er nurse Zach · 9 years ago0 0 Comment Asker's rating Yes, one can have both bipolar disorder and schizophrenia, as the cause is one and the same - a spirit (ghost). Not only are the mood swings imparted by the associated spirit, but the alleged hallucinations are as well. The voices that those diagnosed as h... | Dual Diagnosis: Understanding Sex Addiction With Bipolar Disorder Dual Diagnosis: Understanding Sex Addiction With Bipolar Disorder February 5, 2015 Dual Diagnosis Bipolar disorder manifests itself in one college student’s “need” to sexually expose himself on campus. Marty was diagnosed with bipolar 1 disorder in the spring of his junior year in college. The symptoms had emerged during adolescence, but it wasn’t until a particularly startling manic episode that Marty’s doctor knew his depression was more than unipolar (i.e., clinical depression by itself). The gifted art student had painted his naked body in elaborate geometric patterns and shown up at the fountain in front of his university’s grand administrative building during the middle of a sunny afternoon. He proceeded to dramatically quote Michel Foucault’s Madness and Civilization, even as he was carried away by campus security. The combination of SSRIs and mood stabilizers prescribed to Marty for the treatment of bipolar disor... | Understanding Schizoaffective Disorder Medication Understanding Schizoaffective Disorder Medication Because schizoaffective disorder has symptoms of both psychosis and a mood disorder, ✱ doctors often prescribe different medicines to treat different symptoms of the condition. For example, they may prescribe: An antipsychotic, which helps symptoms like delusions and hallucinations A mood-stabilizing medicine, which can help level out “highs” and “lows”An antidepressant, which can help feelings of sadness, hopelessness, and difficulty with sleep and concentration One medicine for schizoaffective disorder's symptoms INVEGA SUSTENNA ® treats the symptoms of schizoaffective disorder (psychosis and mood), so it may be possible for you to manage symptoms with one medicine if your doctor feels it’s right for you. And that means one less pill to think about every day. Approved for the treatment of schizophrenia and schizoaffective disorder.✱ Please discuss your symptoms with your healthcare pro... | Paranoia and schizophrenia: What you need to know Newsletter MNT - Hourly Medical News Since 2003Search Log in Newsletter MNT - Hourly Medical News Since 2003Search Login Paranoia and schizophrenia: What you need to know Last updated Thu 25 May 2017By Yvette Brazier Reviewed by Timothy J. Legg, Ph D, CRNPOverview Symptoms Causes Diagnosis Treatment Complications A person who has a condition on the schizophrenia spectrum may experience delusions and what is commonly known as paranoia. These delusions may give rise to fears that others are plotting against the individual. Everyone can have a paranoid thought from time to time. On a rough day, we may find ourselves saying "Oh boy, the whole world is out to get me!" But we recognize that this is not the case. People with paranoia often have an extensive network of paranoid thoughts and ideas. This can result in a disproportionate amount of time spent thinking up ways for the individual to protect themselves from their perceived persecutors... | Same Genes Suspected in Both Depression and Bipolar Illness Same Genes Suspected in Both Depression and Bipolar Illness Increased Risk May Stem From Variation in Gene On/Off Switch January 28, 2010 • Science Update Protein produced by PBRM1 gene Researchers, for the first time, have pinpointed a genetic hotspot that confers risk for both bipolar disorder and depression. People with either of these mood disorders were significantly more likely to have risk versions of genes at this site than healthy controls. One of the genes, which codes for part of a cell's machinery that tells genes when to turn on and off, was also found to be over-expressed in the executive hub of bipolar patients' brains, making it a prime suspect. The results add to mounting evidence that major mental disorders overlap at the molecular level. "People who carry the risk versions may differ in some dimension of brain development that may increase risk for mood disorders later in life," explained Francis Mc Mahon, M... | Schizophrenia Definition and Characteristics Schizophrenia Schizophrenia Definition and Characteristics Symptoms, Treatments and Risk Factors By Marcia Purse | Reviewed by Steven Gans, MDUpdated July 06, 2017Share Pin Email Print Kent Mathews/Stone/Getty Images Schizophrenia is a severe, lifelong mental disorder characterized by delusions, hallucinations, incoherence and physical agitation. It is classified as a thought disorder, while bipolar disorder is a mood disorder. Incidence and Risk Factors for Schizophrenia It is estimated that 1% of the world's population has schizophrenia. While there is evidence that genetic factors have a role in developing schizophrenia, environment may play a significant part as well. The Difference Between Bipolar Disorder and Schizophrenia While bipolar I disorder may include psychotic features similar to those found in schizophrenia during manic or depressive episodes, and bipolar II disorder during depressive episodes, schizophrenia does not include ... | +* Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "cos_sim" + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 2048 +- `per_device_eval_batch_size`: 2048 +- `learning_rate`: 0.0002 +- `num_train_epochs`: 2 +- `warmup_ratio`: 0.05 +- `bf16`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 2048 +- `per_device_eval_batch_size`: 2048 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 1 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 0.0002 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 2 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.05 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: True +- `logging_nan_inf_filter`: True +- `save_safetensors`: True +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: True +- `fp16`: False +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `local_rank`: 5 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: True +- `dataloader_num_workers`: 0 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: None +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: False +- `resume_from_checkpoint`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_private_repo`: None +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `include_for_metrics`: [] +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `dispatch_batches`: None +- `split_batches`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `eval_on_start`: False +- `use_liger_kernel`: False +- `eval_use_gather_object`: False +- `average_tokens_across_devices`: False +- `prompts`: None +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | +|:------:|:----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| +| 0.0185 | 2 | 8.9197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0370 | 4 | 8.4814 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0556 | 6 | 6.6919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0741 | 8 | 5.2493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0926 | 10 | 4.2792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1111 | 12 | 3.4554 | 0.2385 | 0.3867 | 0.7209 | 0.3194 | 0.5207 | 0.4438 | 0.1702 | 0.3732 | 0.8791 | 0.2758 | 0.4377 | 0.4026 | 0.4623 | 0.4331 | +| 0.1296 | 14 | 3.0437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1481 | 16 | 2.6133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1667 | 18 | 2.3395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1852 | 20 | 2.1826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2037 | 22 | 2.0498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2222 | 24 | 1.9743 | 0.2706 | 0.4493 | 0.8104 | 0.4201 | 0.6036 | 0.5542 | 0.2249 | 0.5859 | 0.9221 | 0.3091 | 0.5671 | 0.5562 | 0.4864 | 0.5200 | +| 0.2407 | 26 | 1.9111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2593 | 28 | 1.8534 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2778 | 30 | 1.8137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2963 | 32 | 1.7587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3148 | 34 | 1.7124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3333 | 36 | 1.6841 | 0.2945 | 0.4652 | 0.8333 | 0.4352 | 0.6189 | 0.5619 | 0.2512 | 0.5977 | 0.9403 | 0.3322 | 0.5502 | 0.5778 | 0.4596 | 0.5321 | +| 0.3519 | 38 | 1.6765 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3704 | 40 | 1.6314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3889 | 42 | 1.5989 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4074 | 44 | 1.592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4259 | 46 | 1.572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4444 | 48 | 1.5525 | 0.3045 | 0.4626 | 0.8526 | 0.4507 | 0.6275 | 0.5617 | 0.2575 | 0.5676 | 0.9406 | 0.3661 | 0.5666 | 0.5693 | 0.4231 | 0.5346 | +| 0.4630 | 50 | 1.51 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4815 | 52 | 1.5156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5 | 54 | 1.5076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5185 | 56 | 1.4781 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5370 | 58 | 1.4833 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5556 | 60 | 1.4576 | 0.3042 | 0.4727 | 0.8456 | 0.4578 | 0.6338 | 0.5599 | 0.2513 | 0.5883 | 0.9370 | 0.3792 | 0.5656 | 0.5229 | 0.4431 | 0.5355 | +| 0.5741 | 62 | 1.4402 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5926 | 64 | 1.438 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.6111 | 66 | 1.4504 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.6296 | 68 | 1.4142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.6481 | 70 | 1.4141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.6667 | 72 | 1.3917 | 0.3225 | 0.4697 | 0.8632 | 0.4529 | 0.6474 | 0.5575 | 0.2341 | 0.5942 | 0.9464 | 0.3846 | 0.5467 | 0.4924 | 0.4124 | 0.5326 | +| 0.6852 | 74 | 1.4108 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.7037 | 76 | 1.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.7222 | 78 | 1.385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.7407 | 80 | 1.3946 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.7593 | 82 | 1.3762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.7778 | 84 | 1.3606 | 0.3325 | 0.4747 | 0.8730 | 0.4891 | 0.6511 | 0.5941 | 0.2530 | 0.5835 | 0.9452 | 0.3776 | 0.5490 | 0.4680 | 0.4447 | 0.5412 | +| 0.7963 | 86 | 1.3615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.8148 | 88 | 1.3811 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.8333 | 90 | 1.3462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.8519 | 92 | 1.3617 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.8704 | 94 | 1.3345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.8889 | 96 | 1.3291 | 0.3249 | 0.4780 | 0.8791 | 0.4925 | 0.6518 | 0.6018 | 0.2678 | 0.5981 | 0.9451 | 0.3799 | 0.5474 | 0.4423 | 0.4340 | 0.5418 | +| 0.9074 | 98 | 1.3253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.9259 | 100 | 1.3375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.9444 | 102 | 1.3177 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.9630 | 104 | 1.3318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.9815 | 106 | 1.297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.0093 | 108 | 1.3128 | 0.3211 | 0.4761 | 0.8869 | 0.4904 | 0.6531 | 0.5906 | 0.2660 | 0.6035 | 0.9473 | 0.3810 | 0.5749 | 0.4420 | 0.4286 | 0.5432 | +| 1.0278 | 110 | 1.3088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.0463 | 112 | 1.3071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.0648 | 114 | 1.2936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.0833 | 116 | 1.2839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.1019 | 118 | 1.2693 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.1204 | 120 | 1.291 | 0.3022 | 0.4793 | 0.8822 | 0.5117 | 0.6691 | 0.5708 | 0.2637 | 0.6140 | 0.9521 | 0.3913 | 0.5773 | 0.4487 | 0.4281 | 0.5454 | +| 1.1389 | 122 | 1.2636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.1574 | 124 | 1.2427 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.1759 | 126 | 1.2167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.1944 | 128 | 1.202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.2130 | 130 | 1.1931 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.2315 | 132 | 1.178 | 0.2842 | 0.4731 | 0.8755 | 0.5114 | 0.6814 | 0.5611 | 0.2731 | 0.6122 | 0.9477 | 0.3926 | 0.5723 | 0.4647 | 0.4441 | 0.5457 | +| 1.25 | 134 | 1.1955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.2685 | 136 | 1.18 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.2870 | 138 | 1.1771 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.3056 | 140 | 1.173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.3241 | 142 | 1.141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.3426 | 144 | 1.1531 | 0.2816 | 0.4822 | 0.9067 | 0.5164 | 0.6609 | 0.5758 | 0.2713 | 0.6295 | 0.9596 | 0.4018 | 0.5862 | 0.4615 | 0.4309 | 0.5511 | +| 1.3611 | 146 | 1.1608 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.3796 | 148 | 1.1489 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.3981 | 150 | 1.1531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.4167 | 152 | 1.1391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.4352 | 154 | 1.1405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.4537 | 156 | 1.1336 | 0.3180 | 0.4810 | 0.8891 | 0.5077 | 0.6655 | 0.5609 | 0.2797 | 0.5979 | 0.9557 | 0.3988 | 0.6011 | 0.5093 | 0.4176 | 0.5525 | +| 1.4722 | 158 | 1.1165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.4907 | 160 | 1.1316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.5093 | 162 | 1.1328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.5278 | 164 | 1.1229 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.5463 | 166 | 1.1312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.5648 | 168 | 1.1112 | 0.2801 | 0.4865 | 0.9104 | 0.5040 | 0.6631 | 0.5666 | 0.2847 | 0.6059 | 0.9599 | 0.4003 | 0.5906 | 0.4927 | 0.4312 | 0.5520 | +| 1.5833 | 170 | 1.1304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.6019 | 172 | 1.1257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.6204 | 174 | 1.139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.6389 | 176 | 1.1116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.6574 | 178 | 1.1161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.6759 | 180 | 1.1024 | 0.2991 | 0.4822 | 0.9009 | 0.4886 | 0.6652 | 0.5659 | 0.2577 | 0.6147 | 0.9597 | 0.4051 | 0.5747 | 0.4585 | 0.4207 | 0.5456 | +| 1.6944 | 182 | 1.1239 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.7130 | 184 | 1.1266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.7315 | 186 | 1.1154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.75 | 188 | 1.1382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.7685 | 190 | 1.102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.7870 | 192 | 1.1046 | 0.3107 | 0.4764 | 0.9040 | 0.4828 | 0.6680 | 0.5747 | 0.2625 | 0.5969 | 0.9567 | 0.3948 | 0.5801 | 0.4641 | 0.4313 | 0.5464 | +| 1.8056 | 194 | 1.1241 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.8241 | 196 | 1.1266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.8426 | 198 | 1.1257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.8611 | 200 | 1.1148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.8796 | 202 | 1.1133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.8981 | 204 | 1.1149 | 0.2840 | 0.4733 | 0.9203 | 0.4901 | 0.6700 | 0.5747 | 0.2547 | 0.6061 | 0.9594 | 0.3972 | 0.5856 | 0.4572 | 0.4180 | 0.5454 | +| 1.9167 | 206 | 1.1122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.9352 | 208 | 1.1259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.9537 | 210 | 1.1215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.9722 | 212 | 1.1047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 1.9907 | 214 | 1.1166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | + +
+ +### Framework Versions +- Python: 3.11.9 +- Sentence Transformers: 3.3.1 +- Transformers: 4.48.0.dev0 +- PyTorch: 2.6.0.dev20241112+cu121 +- Accelerate: 1.2.1 +- Datasets: 2.21.0 +- Tokenizers: 0.21.0 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@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 +```bibtex +@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} +} +``` + + + + + + \ No newline at end of file