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ModernBERT-base-DPR-fullneg-gte-0.0002

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:1770649
loss:CachedMultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
ModernBERT-base-DPR-fullneg-gte-0.0002 / README.md
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---
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
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **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)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](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 [<code>NanoBEIREvaluator</code>](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 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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: <code>anchor</code>, <code>positive</code>, <code>negative_0</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, and <code>negative_4</code>
* 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 | <ul><li>min: 4 tokens</li><li>mean: 20.15 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 173.18 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 170.06 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 167.88 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 167.95 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 166.32 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 167.63 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive | negative_0 | negative_1 | negative_2 | negative_3 | negative_4 |
|:-----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What happens if you eat raw chicken?</code> | <code>What are the dangers of eating raw chicken?</code> | <code>Does all raw chicken have salmonella?</code> | <code>How safe is to eat chicken during pregnancy?</code> | <code>What meats are safe to eat raw?</code> | <code>What are some natural obligations of chicken?</code> | <code>Is it safe to eat raw egg?</code> |
| <code>how long does it take for a wren egg to hatch</code> | <code>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.</code> | <code>- 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.</code> | <code>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.</code> | <code>- 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.</code> | <code>- 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.</code> | <code>- 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.</code> |
| <code>can you have schizophrenia and bipolar</code> | <code>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...</code> | <code>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...</code> | <code>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...</code> | <code>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...</code> | <code>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...</code> | <code>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 ...</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](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
<details><summary>Click to expand</summary>
- `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
</details>
### Training Logs
<details><summary>Click to expand</summary>
| 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 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
</details>
### 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}
}
```
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