---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:221599363
- loss:MultipleNegativesRankingLoss
base_model: thebajajra/RexBERT-large
widget:
- source_sentence: I found out that this novel was based on real ...
sentences:
- I found out that this novel was based on real people only by reading the afterword.
This is a tremendously important piece of information about the book.
- "I recently got a mbp 16 and although I’m very impressed by the speakers I still\
\ wanted to purchase a set of external speakers for the desk setup. The thing\
\ is since these are so good I don’t even know at which price point I should be\
\ shopping to get something better. \n\nThe other day a youtuber I watch said\
\ that he has been using the mbp 16 speakers instead of his $200 speakers because\
\ he doesn’t feel the need to anymore.\n\nSo, is a pair of $60 speakers going\
\ to be better or do I need to go higher in price to really hear a difference?"
- Larry A Winters is a real good story teller. His use and knowledge of Jessie
Black as the heroine indicates a familiarity that makes the reader wonder if Larry
and Jessie are one and the same. A real page turner but not quite in the cant
put it down stage.
- source_sentence: Excellent seller but product did not work and returned for ...
sentences:
- Summary The K-II continuous-sample-flow-with-banding-9 zonal centrifuge hasbeen
developed for large-scale virus isolation. The cylindrical aluminum rotor (capacity
3600 ml) contains a 3 liter gradient and has a 700 ml stream volume. Fluid line
seals are located on opposite ends of the rotor, eliminating the possibility of
cross-leakage. An air turbine drive is used to accelerate the rotor to 35,000
rpm (83,440 g , maximum). The development of safe armor and control systems is
described.
- "I have a 2015 model and both front turn signals are busted after a wheelie mistake.\
\ \nThe left one doesn't light up and the right one is missing. \nWhere can\
\ I get the original stock turn signals?"
- Excellent seller but product did not work and returned for a full refund. Did
not pair up with my ATT box. Was with a tech from ATT but we both could not get
it to pair up.
- source_sentence: where will the men's rings be held in 2016 olympics
sentences:
- the event of there being more than two gymnasts from same NOC, only the first
two ranked among them would qualify for the final, with the next best ranked gymnast
qualifying instead. Gymnastics at the 2016 Summer Olympics – Men's rings The Men's
rings competition at the 2016 Summer Olympics was held at the HSBC Arena on 15
August. The medals were presented by Bernard Rajzman IOC member, Brazil and Wolfgang
Willam, FIG Executive Committee Member. The top 8 qualifiers in the qualification
phase (limit two per NOC), based on combined score of each apparatus, advanced
to the individual all-around
- Russians marked the anniversary of the 1917 Bolshevik Revolution on Sunday with
marches, Communist rallies and protests against a parliamentary proposal to scrap
what was once the most sacred Soviet holiday.
- 'The only reason I could think of is the system has to log each individual painting
as it’s own but since the same asset doesn’t require any new space and all the
strokes are memorized anyway as you can move them individually, is there any real
difference aside from perhaps the mild tedium of one way or the other?
Just one of the small questions that keep me up at night.'
- source_sentence: So back to a semi-normal profile.
sentences:
- but suffers from uneven pacing. It's dark in tone, but not as arresting as, say,
3 AM or Damiano's Devil in Miss Jones. I'm glad I saw it, but there's little chance
I'll ever revisit (except, maybe, to check out that Graham/Colt scene...)." The
Story of Joanna The Story of Joanna is a 1975 pornographic film directed by Gerard
Damiano and starring Jamie Gillis and Terri Hall. The film has a sado-masochism
theme influenced by "Story of O" (1954). It is considered one of the classics
of the Golden Age of Porn (1969–1984). It has been inducted into the XRCO
- "\n\nIntroduction\n\nMost medical schools across the globe use academic achievement\
\ as the primary selection criteria for admission into medical school. 1 This\
\ also applies to all medical schools in Nepal where entrance examinations conducted\
\ by the universities or Academies are based on general science subjects. 2,3\
\ However, academic achievement alone as the predictor of someone becoming a 'good'\
\ physician has been questioned by many. [3][4][5][6][7][8] Certainly, personal\
\ qualities play an immense role in medical practice, which in itself is a complex\
\ phenomenon. [9][10] Patan Academy of Health Sciences (PAHS) is an autonomous,\
\ health sciences institute established in 2008 in Nepal with a mandate to improve\
\ the health of people in rural Nepal by producing health professionals who were\
\ competent, compassionate, and willing to serve in rural Nepal. It was clear\
\ from the outset that academic attainment alone among the aspirants for medical\
\ school was not going to be enough as admission criteria for the School of Medicine\
\ of PAHS. Further, PAHS also determined the desired characteristics/attributes\
\ of its graduates involving all the stakeholders a priori. 11 In this respect,\
\ a psychometric test battery (Personal Qualities Assessment, PQA 9-10 ) and an\
\ Admission OSCE [12][13] procedures were explored to see if they could be used\
\ to select medical students for PAHS. This paper reports the validation of the\
\ PQA test battery using science and health sciences students as they represented\
\ the majority of prospective applicants for the PAHS undergraduate medical education\
\ program commonly known as Bachelor in Medicine and Bachelor in Surgery (MBBS)\
\ in Nepal/South Asia, as well as non-science students of public/community schools.\
\ 14\n\n\nMethod\n\nThe Personal Qualities Assessment (PQA) test battery is used\
\ commercially to select health science students in many countries around the\
\ world (http://www.pqa.net.au/research.html) and found to be valid, reliable\
\ and predictive across different population. [9][10] The PQA test battery tests\
\ the cognitive ability through PQA Test A1 or Mental Agility Test (MAT) and a\
\ range of non-cognitive qualities though PQA Test A2 (Moral Orientation for Justice\
\ and Care: MOJAC) and PQA Test A3 (Empathy, Confidence, Aloofness and Narcissism:\
\ ECAN). 15 PAHS, Nepal and PQA Innovation, Australia collaborated to locally\
\ validate and use PQA test for selecting medical students of School of Medicine,\
\ PAHS in early 2008. PAHS conducted the pilot tests of PQA test batteries using\
\ Optical Mark Reader (OMR) sheets, scanned them, created raw file and, sent it\
\ as secured spreadsheet file to PQA team after the test. The PQA team then scored\
\ the tests using pre-defined keys and rules and, send them back as secured spreadsheet\
\ and report files to PAHS for further processing.\n\nIn order to validate the\
\ PQA tools in local context, they were forward-translated to Nepali by a professional\
\ bilingual person and was back-translated to English by another bilingual professional\
\ under the aegis of PAHS Admission team formed in 2008. The original PQA and\
\ back-translated PQA tools were then discussed iteratively among PAHS Admission\
\ and PQA teams before finalizing the Nepali version with consensus.\n\nThe Personal\
\ Qualities Assessment tests in Nepali language was pretested with volunteer 10+2\
\ non-science students of public/community schools located inside (n=75) and outside\
\ of Kathmandu valley (n=95) and volunteer 10+2 science students of a public school\
\ located outskirts of Kathmandu (n=35). As per PQA norm, only the volunteer students\
\ completing 80% and above items on Test A2 and Test A3 were included in the final\
\ analysis, whereas data of all the volunteer students on Test A1 was included\
\ in the final analysis. These tests were scored using the pre-defined keys and\
\ rules in Australia by the PQA team. Ethical approval was obtained from the Institutional\
\ Review Committee of PAHS (Ref: phs2204081608).\n\nJournal of Patan Academy of\
\ Health Sciences. 2022Aug;9(2):95-101.\n\nThe Personal Qualities Assessment Test\
\ A1 (48 items with complex verbal, numerical, spatial, and abstract reasoning)\
\ and PQA Test A4 (90 items with simple verbal, numerical, spatial, and abstract\
\ reasoning) in English and Nepali languages were trailed again with the larger\
\ pool of volunteer 10+2 science students (n=131) of two community colleges and\
\ 10+3 health science students (n=56) of a Government College, both located outside\
\ of Kathmandu valley. Descriptive statistics were used to describe the test scores\
\ whereas the ttest was used to compare the test scores between groups. A p-value\
\ less than 0.05 was taken as a statistically significant result.\n\n\nResult\n\
\nThe Personal Qualities Assessment tests in Nepali were pre-tested first with\
\ 205 (110 males and 95 females) 10+2 non-science students in 2008, and Test A1\
\ and Test A4 in English and Nepali were pre-tested again with 187(141 males and\
\ 46 females) 10+2 science and 10+3 health science students in 2009. The mean±standard\
\ deviation (range) of students' age (in years) in the first and second samples\
\ were 20.3±1.3 (17-24) and 18.5±3.4 .\n\nThe cognitive ability test (PQA Test\
\ A1/MAT) had lower mean±SD scores 15.3±3.7 than the norm 27.6±5.6 (multinational\
\ pool of 1187 applicants to medical schools). The range revealed the minimum\
\ and maximum scores as 7 and 25 with a median of 15. The internal consistency\
\ reliability coefficient (Cronbach's Alpha) was very low (0.27) for the first\
\ pre-test samples.\n\nOn the other hand, the non-cognitive personality tests\
\ had comparable mean±SD scores of 109.4±13.9 for Test A2 and 259.5± 20.1 for\
\ Test A3 with the multinational norms. Most importantly, the Coefficient alpha\
\ or the internal consistency reliability of Test A2 and Test A3 were greater\
\ than 0.80 (higher than the minimum value of 0.70) for both the tests. Further,\
\ a significant and low degree of negative correlation (r=-0.153, p=0.028) was\
\ found between Test A1 and Test A3 whereas a non-significant low negative correlation\
\ was observed between Test A1 and Test A2 (r=-0.125, p=0.074). As Personal Qualities\
\ Assessment Test A1/MAT score in Pre-Test I followed a normal distribution (Shapiro-Wilk=0.987,\
\ p-value=0.068) and both science and non-science groups had equal variance (Levene's\
\ F=0.421, p-value=0.517), independent samples t-test was used to compare Test\
\ A1 score between science and non-science students, Table 2. Test A1 scores were\
\ found to be higher for science students and the result was highly significant\
\ statistically (t-test=-3.963, p-value<0.0001). The scatterplot of the standardized\
\ scores (zscores) of Test A2/MOJAC and Test A3/ECAN in Nepal language from non-science\
\ students shows that most of these students' LibCom (total of MOJAC) and ECAN\
\ z-scores lie between -2 and +2 SD and few students' scores were outside of this\
\ range, Figure 1. The Test A1 (English language) scores for 10+3 health science\
\ students and 10+2 science students were not significantly different but Test\
\ A4 (English language) scores for 10+3 health science students and 10+2 science\
\ students were statistically different in the second pre-test, Table 4. \n\n\
\nDiscussion\n\nThe MAT (Test A1) score was found to be lower than the international\
\ norms for both 10+2 science as well as non-science students, which suggests\
\ unfamiliarity with this form of test, differences in schooling, general cultural\
\ differences in approach to tests, etc. among these public/community school students.\
\ As the Test A1 questions were based on complex verbal, numerical, spatial, and\
\ abstract reasoning dimensions, it suggests that higher school students of Nepal\
\ require more exposure and practice on these types of aptitude tests as they\
\ are used widely to select students, screen recruits for military/police forces,\
\ and test job applicants. 16,17 The MAT (Test A1) in Nepali language scores were\
\ found to be significantly higher among higher secondary level science students\
\ compared to non-science students, possibly due to mathematical intuition leading\
\ to plausible numerical and abstract reasoning as part of their courses rather\
\ than higher verbal and spatial reasoning abilities. The MAT in the Nepali language\
\ had a low internal consistency reliability coefficient in the first pre-test,\
\ indicating that the different types of items i.e., verbal, numerical, spatial,\
\ and abstract reasoning items included in the test were of differing difficulties\
\ for this group, who may have guessed many of their answers. Also, possibly the\
\ volunteers felt that since their future was not at stake, they did not feel\
\ the need to fully exercise their intellectual ability in answering the questions,\
\ as most of them (170 out of 205) were non-science students. It may also be true\
\ that Test A1 in the Nepali language is not a suitable cognitive ability test\
\ for the 10+2 science as well as 10+2 nonscience students.\n\nOn the other hand,\
\ MOJAC (Test A2) and NACE (Test A3) scores in Pre-Test I were similar to the\
\ international norm and had very high internal construct reliability (>0.80)\
\ suggesting they are satisfactory tests for Nepali applicants at 10+2 level or\
\ equivalent in both science and nonscience streams. These tests could be used\
\ to deselect outliers, i.e., students with potential behavior problems where\
\ outliers were Journal of Patan Academy of Health Sciences. 2022Aug;9(2):95-101.\n\
\ndefined statistically as below -2 SD and above +2 SD for the standardized total\
\ MOJAC (LibCom) and ECAN scores. 15 Tests A2 and Test A3 had small negative but\
\ statistically insignificant correlations with Test A1, showing that they measured\
\ different traits (Test A1 measuring cognitive abilities and Test A2 and A3 measure\
\ non-cognitive traits) and thus are both potentially useful for selecting students\
\ for all the undergraduate level health-related programs in Nepal.\n\nIronically,\
\ the small negative correlation further indicates that there is a slight tendency\
\ for those who are stronger in cognitive skills to be weaker in interpersonal\
\ skills, but there were still a substantial proportion of applicants who are\
\ strong in both.\n\nDuring the second pre-test done to check the consistency\
\ of the first pre-test results, Test A1 in Nepali was again found to have a slightly\
\ low internal reliability coefficient (Alpha < 0.50) for a larger pool of n=89\
\ of volunteer science and health science students whereas the original MAT/Test\
\ A1 in the English language had a slightly more acceptable internal construct\
\ reliability (Alpha>0.60) for n=98 volunteer science and health science students.\
\ This result is similar to 2003 Scottish medical school applicants 10 though\
\ it is lower than the PQA international norm student sample average of 0.73.\
\ 15 Coefficient alpha of 0.60 and above is considered good and 0.70 and above\
\ is considered very good for tests with complex items i.e., MAT (Test A1) in\
\ the English language used in Nepal. 18 Further, Test A4 in the English language\
\ showed statistically different and higher results for science and health sciences\
\ students compared to Test A4 in the Nepali language whereas Test A1 in the English\
\ language showed higher but statistically insignificant results compared to Test\
\ A1 in the Nepali language. So, Test A4 containing simple verbal, numerical,\
\ spatial, and abstract reasoning items is found to be easy whereas Test A1 containing\
\ complex verbal, numerical, spatial, and abstract reasoning items is found to\
\ be difficult for both groups of students, Figure 1.\n\nWhen the Test A1 and\
\ Test A4 test scores in the English language were analyzed separately for the\
\ science and health science students, Test A1 scores were not found to be statistically\
\ different indicating a fair test to select undergraduate medical students compared\
\ to Test A4 which produced a statistically different score. Thus, MAT (Test A1)\
\ in the English version was chosen to select MBBS students of the School of Medicine,\
\ Patan Academy of Health Sciences as it had sufficient internal consistency reliability\
\ and was fair to both science and health science students, despite being a bit\
\ difficult test of verbal, numerical, spatial and abstract reasoning aptitude\
\ required for the course. Recent studies confirmed the predictive validity of\
\ PQA tests among medical students in the UK, which remains to be done at PAHS.\
\ [18][19][20] \n\n\nConclusion\n\nThe MAT (PQA Test A1) in English was found\
\ to be a reliable test to select medical students for PAHS and similar institutions\
\ in Nepal as it was also found to be fair among 10+2 science/10+3 health science\
\ students. Also, PQA Test A2 and Test A3 in Nepali were found to be fair and\
\ reliable tests to identify unusual personality traits and to deselect such candidates\
\ for all the undergraduate level health science programs in Nepal and beyond.\n\
\n\nPAHS Admission committee conducted the second Pre-Test of Test A1 and Test\
\ A4 in Nepali and English languages with the large (187) volunteer 10+2 science\
\ students and 10+3 health science students in public school/college outside of\
\ Kathmandu valley as Nepali Test A1 results were not promising with the non-science\
\ and the science students. The Mental Agility Test (Test A1) had lower mean±SD\
\ scores for English 18.4±5.0 language and Nepali 16.8±4.3 language than the multinational\
\ norm of 27.6±5.6. Yet, Test A1 in the English language's mean score of 18.4\
\ was different from Test A1 in Nepali's mean score of 16.8, which was also statistically\
\ significant (t=2.3348, p-value=0.0206). Although the mean of Test A1 in the\
\ English language (18.4) done in 2009 was found to be higher than Test A1 in\
\ the Nepali language (17.51) for the 10+2 Test A1 in the English language and\
\ 0.49 for the Nepali language for the second pre-test samples. On the other hand,\
\ the mean percentage score of the General Ability Test (Test A4) in English language\
\ and Nepali Journal of Patan Academy of Health Sciences. 2022Aug;9(2):95-101.\
\ language were 50.0 and 47.6 respectively, which was higher than the mean percentage\
\ score of Test A1 in the English language (38.3) and Nepali language (35.0).\
\ The mean percentage scores were statistically different for English and Nepali\
\ versions of Test A4 (t=2.310, p-value=0.022) but it was not statistically different\
\ for English and Nepali versions of Test A1 (t=1.367, p-value=0.171). Further\
\ analysis of Test A1 and Test A4 scores in English and Nepali languages with\
\ the sex of the applicants was not found to be statistically different. However,\
\ Test A1 and Test A4 showed statistically significant negativescience students\
\ in 2008, they were not \nsignificantly \ndifferent \n(t=0.9769, \np-\nvalue=0.3304),\
\ Table 2 & 3. The internal \nconsistency reliability (coefficient alpha), a \n\
proxy for construct validity, was found to be \n0.63 for correlations with the\
\ age of the students for \nEnglish (r=-0.226, p-value=0.025; r=-0.395, p-\nvalue<0.001)\
\ and Nepali (r=-0.261, p-\nvalue=0.011; \nr=-0.211, \np-value=0.047) \nlanguages.\
\ \n\n\n\nTable 1 .\n1Personal qualities assessment pre-test I with non-science\
\ major students, 2008, NepalTable 2. Test A1 (Nepali) score of 10+2 science and\
\ non-science students, 2008Test (Language) \nNepali candidates \nInternational\
\ candidates \nN Mean \nSD \nMedian \nRange Alpha \nN Mean \nSD \nTest A1-NEPALI\
\ \n205 \n15.3 \n3.7 \n15.0 \n7 -25 \n0.27 \n1811 \n27.6 \n5.6 \nTest A2-NEPALI\
\ \n205 109.4 \n13.9 \n108.0 \n79 -147 \n0.82 \n9762 116.0 \n15.3 \nTest A3-NEPALI\
\ \n205 259.6 \n20.1 \n259.0 \n216 -320 \n0.81 \n7032 283.0 \n22.8 \n\nStream\
\ \nN Mean \nSD Median \nMin \nMax \nt \np-value \nNon-sciences -NEPALI (48 items)\
\ \n170 14.87 3.643 \n15.00 \n7 \n24 \n3.963 \n<0.0001 \nScience -NEPALI (48 items)\
\ \n35 17.51 3.338 \n17.00 \n12 \n25 \nTotal \n205 15.32 3.721 \n15.00 \n7 \n\
25 \n\n\n\nTable 3 .\n3Personal qualities assessment pre-test II with science\
\ and health science students, 2009, NepalTest (Language) \nNepali candidates\
\ \nInternational candidates \nN \nMean \nSD \nMedian \nRange Alpha \nN Mean \n\
SD \nTest A1-ENGLISH \n(48 items) \n\n98 \n18.4 \n5.0 \n18.0 \n8 -30 \n0.63 \n\
1811 \n27.6 \n5.6 \n\nTest A1-NEPALI \n(48 items) \n\n89 \n16.8 \n4.3 \n17.0 \n\
8 -25 \n0.49 \nNA \n\nTest A4-ENGLISH \n(90 items) \n\n98 \n45.0 \n11.3 \n44.5\
\ \n18 -77 \n0.88 \nNA \n\nTest A4-NEPALI \n(90 items) \n\n89 \n42.8 \n10.1 \n\
43.0 \n12 -60 \nNA \nNA \n\nNA=Not Available \n\n\n\nTable 4 .\n4Comparison of\
\ mental agility test (test A1) and general ability test (test A4) (English version)\
\ scores \namong science and health sciences students, pre-test II, 2009, Nepal\
\ \n\nTest and Stream \n(Test: Discipline) \n\nNepal Candidates \nN \nMean \n\
SD \nSEM \nMedian \nRange \nT \np-value \nTest A1-ENGLISH: \n10+3 Health Sciences\
\ \n\n56 \n18.3 \n4.5 \n0.61 \n17.0 \n12 -29 \n\n0.342 \n0.772 \nTest A1-ENGLISH:\
\ \n10+2 Sciences \n\n42 \n18.6 \n5.6 \n0.87 \n18.5 \n8 -30 \n\nTest A4-ENGLISH:\
\ \n10+3 Health Sciences \n\n56 \n41.77 \n9.3 \n1.25 \n42.5 \n18 -60 \n\n3.292\
\ \n0.002 \nTest A4-ENGLISH: \n10+2 Sciences \n\n42 \n49.24 \n12.3 \n1.90 \n51.5\
\ \n24 -77 \n\nSEM = Standard error of the measurement \n\n\nAcknowledgmentWe\
\ are grateful to the officials of PAHS and the two Admission Teams for their\
\ help during this study. We are thankful to all the faculty and fellows at PSG\
\ FAIMER Regional Institute, Coimbatore, India for their inputs and feedback as\
\ this study was conducted as part of the educational innovation project of the\
\ first author to fulfill the partial requirement for the FAIMER fellowship in\
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\ Health Sci Educ. 62| DOI | PubMed | Google Scholar | WeblinkSalvatori P. Reliability\
\ and validity of admissions tools used to select students for the health professions.\
\ Adv Health Sci Educ. 2001;6(2):159-75. | DOI | PubMed | Google Scholar | Weblink\
\ |\n\nA comprehensive model for the selection of medical students. M Bore, D\
\ Munro, D Powis, https:/www.tandfonline.com/doi/abs/10.3109/01421590903095510Med\
\ Teach. 3112| DOI | PubMed | Google Scholar | WeblinkBore M, Munro D, Powis D.\
\ A comprehensive model for the selection of medical students. Med Teach. 2009;31(12):1066-72.\
\ | DOI | PubMed | Google Scholar | Weblink |\n\nRole of public and private schools\
\ in developing of IQ among elementary students. M Batool, M A Dahar, M I Ali,\
\ | Weblink | Full Text. 82Batool M, Dahar MA, Ali MI. Role of public and private\
\ schools in developing of IQ among elementary students. IJSER. 2017;8(2):248-62.\
\ | Weblink | Full Text |\n\nDevelopment and validation of measures of noncognitive\
\ college student potential. The College Board. N Schmitt, A Billington, J Keeney,\
\ M Reeder, T J Pleskac, R Sinha, Research Report. 2011;1. | Full Text | WeblinkSchmitt\
\ N, Billington A, Keeney J, Reeder M, Pleskac TJ, Sinha R, et al. Development\
\ and validation of measures of noncognitive college student potential. The College\
\ Board: Research Report. 2011;1. | Full Text | Weblink |\n\nMaking sense of Cronbach's\
\ alpha. M Tavakol, R Dennick, Int J Med Educ. 2Tavakol M, Dennick R. Making sense\
\ of Cronbach's alpha. Int J Med Educ. 2011;2:53-5.\n\n| Doi | Pubmed, Google\
\ Scholar | Full Text | Weblink |. | DOI | PubMed | Google Scholar | Full Text\
\ | Weblink |\n\nPredictors of professional behavior and academic outcomes in\
\ a UK medical school: A longitudinal cohort study. J Adam, M Bore, R Child, J\
\ Dunn, J Mckendree, D Munro, https:/www.tandfonline.com/doi/abs/10.3109/0142159X.2015.1009023?journalCode=imte20Med\
\ Teach. 379| DOI | PubMed | Google Scholar | Full Text | WeblinkAdam J, Bore\
\ M, Child R, Dunn J, Mckendree J, Munro D, et al. Predictors of professional\
\ behavior and academic outcomes in a UK medical school: A longitudinal cohort\
\ study. Med Teach. 2015;37(9):868-80. | DOI | PubMed | Google Scholar | Full\
\ Text | Weblink |\n\nAssessment for selection for the health care professions\
\ and specialty training: consensus statement and recommendations from the Ottawa.\
\ D Prideaux, C Roberts, K Eva, A Centeno, P Mccrorie, C Mcmanus, Prideaux D,\
\ Roberts C, Eva K, Centeno A, Mccrorie P, Mcmanus C, et al. Assessment for selection\
\ for the health care professions and specialty training: consensus statement\
\ and recommendations from the Ottawa 2010\n\n. https:/www.tandfonline.com/doi/abs/10.3109/0142159X.2011.551560?journalCode=imte20Conference.\
\ Med Teach. 333| DOI | PubMed | Google Scholar | WeblinkConference. Med Teach.\
\ 2011;33(3):215-23. | DOI | PubMed | Google Scholar | Weblink |\n\nThe change\
\ from UMAT to UCAT for undergraduate medical school applicants: impact on selection\
\ outcomes. B Griffin, G L Horton, L Lampe, B Shulruf, W Hu, 10.5694/mja2.50877Med\
\ J Aust. 2142DOI | PubMed | Google Scholar | Full Text | WeblinkGriffin B, Horton\
\ GL, Lampe L, Shulruf B, Hu W. The change from UMAT to UCAT for undergraduate\
\ medical school applicants: impact on selection outcomes. Med J Aust. 2021;214(2):84-9.\
\ | DOI | PubMed | Google Scholar | Full Text | Weblink |\n"
- "Profile\n\nAfter the winking / rating / favoriting experiment, I figured it was\
\ time to go back to my normally scheduled profile with a few of the undertones\
\ left over from the old one. Old Profile\n\nWhat do you guys think? \n\nAlso\
\ it's amazing what a picture of a pet can do. :|"
- source_sentence: Corners are still lifting.
sentences:
- Great product. I purchased this item becuase my wrists would ache after triceps
day at the gym. I would never be able to straighten my wrist and this helped in
fixing that issue.
- These are awesome quart jars. They have a beautiful color, and I use them for
storing soups, nuts and homemade nut milk. I would purchase them again.
- "Hello, I got my ender 3 a little over a year ago and have gotten many successful\
\ prints off of my machine. \n\nI have always had a problem with the corners of\
\ my prints lifting. I originally used a glass plate. That by itself was horrible,\
\ but then I added hairspray, and that worked. The problem was that on long prints,\
\ corners still lifted.\n\nAfter doing this for around 5 months I switched to\
\ a PEI sheet.\n\nThis worked comparably as well as the glass/hairspray combo,\
\ except the corners STILL LIFT on long prints.\n\nNow I have a PEI sheet on boro\
\ glass with an EZABL attached and the corners of my prints are STILL LIFTING.\n\
\nI don't know what i could possibly be doing wrong. The bed must be level. I\
\ get beautiful first layers, which I have tried to \"smudge\" around during printing\
\ and I can confirm that the plastic is being layed down solidly.\n\nIf anyone\
\ could enlighten me as to what is going on I would be thrilled.\n\nI do have\
\ my first layer printing at 30% speed with 150% layer width with the print cooling\
\ fan off as well. Printing PLA at 200C tool temp, 60C bed."
datasets:
- nomic-ai/nomic-embed-unsupervised-data
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on thebajajra/RexBERT-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thebajajra/RexBERT-large](https://huggingface.co/thebajajra/RexBERT-large) on the [nomic-embed-unsupervised-data](https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data) dataset. It maps sentences & paragraphs to a 1024-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:** [thebajajra/RexBERT-large](https://huggingface.co/thebajajra/RexBERT-large)
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [nomic-embed-unsupervised-data](https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data)
- **Language:** en
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
queries = [
"Corners are still lifting.",
]
documents = [
'Hello, I got my ender 3 a little over a year ago and have gotten many successful prints off of my machine. \n\nI have always had a problem with the corners of my prints lifting. I originally used a glass plate. That by itself was horrible, but then I added hairspray, and that worked. The problem was that on long prints, corners still lifted.\n\nAfter doing this for around 5 months I switched to a PEI sheet.\n\nThis worked comparably as well as the glass/hairspray combo, except the corners STILL LIFT on long prints.\n\nNow I have a PEI sheet on boro glass with an EZABL attached and the corners of my prints are STILL LIFTING.\n\nI don\'t know what i could possibly be doing wrong. The bed must be level. I get beautiful first layers, which I have tried to "smudge" around during printing and I can confirm that the plastic is being layed down solidly.\n\nIf anyone could enlighten me as to what is going on I would be thrilled.\n\nI do have my first layer printing at 30% speed with 150% layer width with the print cooling fan off as well. Printing PLA at 200C tool temp, 60C bed.',
'These are awesome quart jars. They have a beautiful color, and I use them for storing soups, nuts and homemade nut milk. I would purchase them again.',
'Great product. I purchased this item becuase my wrists would ache after triceps day at the gym. I would never be able to straighten my wrist and this helped in fixing that issue.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5739, 0.0316, 0.1463]])
```
## Training Details
### Training Dataset
#### nomic-embed-unsupervised-data
* Dataset: [nomic-embed-unsupervised-data](https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data) at [917bae6](https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data/tree/917bae6ed30ebc80fc8c81ba8e3e34558205d6bb)
* Size: 221,599,363 training samples
* Columns: query and document
* Approximate statistics based on the first 1000 samples:
| | query | document |
|:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 6 tokens
- mean: 34.17 tokens
- max: 1024 tokens
| - min: 8 tokens
- mean: 166.07 tokens
- max: 1024 tokens
|
* Samples:
| query | document |
|:------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Effect of steam reforming on methane-fueled chemical looping combustion with Cu-based oxygen carrier | Abstract The reduction characteristics of Cu-based oxygen carrier with H 2 , CO and CH 4 were investigated using a fixed bed reactor, TPR and TGA. Results showed that temperatures for the complete reduction of Cu-based oxygen carrier with H 2 and CO are 300 °C and 225 °C, respectively, while the corresponding temperature with CH 4 is 650 °C. The carbon deposition from CH 4 occurred at over 550 °C. CO-chemisorption experiments were also conducted on the oxygen carrier, and it was indicated that Cu-based oxygen carrier sinter seriously at 700 °C. In order to lower the required reduction temperature of oxygen carriers, a new chemical looping combustion (CLC) process with CH 4 steam reforming has been presented in this paper. The basic feasibility of the process was illustrated using CuO–SiO 2 . The new CLC process has the potential to replace the conventional gas-fired middle- and low-pressure steam and hot water boilers. |
| who appointed onesicritus as chief pilot of the fleet | by the king to hold a conference with the Indian philosophers or Gymnosophists, the details of which have been transmitted to us from his own account of the interview. It was Onesicritus, whom Alexander first sent to summon Dandamis to his court. When later Onesicritus returned empty-handed with the reply of Dandamis, the King went to forest to visit Dandamis. When Alexander constructed his fleet on the Hydaspes, he appointed Onesicritus to the important position of pilot of the king's ship, or chief pilot of the fleet (). Onesicritus held this position not only during the descent of the Indus, |
| when did the madonna of foligno go to paris | Madonna of Foligno hence the name. In 1799 it was carried to Paris, France by Napoleon. There, in 1802, the painting was transferred from panel to canvas by Hacquin and restored by Roser of Heidelberg. A note was made by the restorer: "Rapporto dei cittadini Guijon Vincent Tannay e Berthollet sul ristauro dei quadri di Raffaello conosciuto sotto il nome di Madonna di Foligno." In 1815, after the Battle of Waterloo, it was returned to Italy, where it was placed in the room with the Transfiguration in the Pinacoteca Vaticana of the Vatican Museum in the Vatican City. The painting is a "sacra |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### nomic-embed-unsupervised-data
* Dataset: [nomic-embed-unsupervised-data](https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data) at [917bae6](https://huggingface.co/datasets/nomic-ai/nomic-embed-unsupervised-data/tree/917bae6ed30ebc80fc8c81ba8e3e34558205d6bb)
* Size: 1,113,579 evaluation samples
* Columns: query and document
* Approximate statistics based on the first 1000 samples:
| | query | document |
|:--------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 5 tokens
- mean: 31.98 tokens
- max: 1024 tokens
| - min: 6 tokens
- mean: 161.48 tokens
- max: 1024 tokens
|
* Samples:
| query | document |
|:----------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Concise methods for the synthesis of chiral polyoxazolines and their application in asymmetric hydrosilylation | Seven polyoxazoline ligands were synthesized in high yield in a one-pot reaction by heating polycarboxylic acids or their esters and chiral β-amino alcohols under reflux with concomitant removal of water or the alcohol produced in the reaction. The method is much simpler and more efficient in comparison to those methods reported in the literature.The compounds were used as chiral ligands in the rhodium-catalyzed asymmetric hydrosilylation of aromatic ketones, and the effects of the linkers and the substituents present on the oxazoline rings on the yield and enantioselectivity investigated. Compound 2 was identified as the best ligand of this family for the hydrosilylation of aromatic ketones. |
| On the road to a stronger public health workforce: visual tools to address complex challenges. | The Public Health Workforce Taxonomy: Revisions and Recommendations for Implementation |
| 140mm Jetflo fan availability? | I recently purchased a Nepton 280L, and would like to install an additional pair of 140mm Jetflo fans. Unfortunately they don't seem to be currently available, will they be in the future?
Thank you so much!
PS - I'm loving the cooling system! |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 192
- `per_device_eval_batch_size`: 128
- `learning_rate`: 1e-05
- `num_train_epochs`: 4
- `warmup_steps`: 1000
- `bf16`: True
- `dataloader_num_workers`: 20
- `dataloader_prefetch_factor`: 4
- `ddp_find_unused_parameters`: False
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 192
- `per_device_eval_batch_size`: 128
- `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`: 1e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 1000
- `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
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 20
- `dataloader_prefetch_factor`: 4
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `ddp_find_unused_parameters`: False
- `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
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
Click to expand
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:------:|:-------------:|:---------------:|
| 0.0007 | 100 | 3.8319 | - |
| 0.0014 | 200 | 1.5773 | - |
| 0.0021 | 300 | 0.2044 | - |
| 0.0028 | 400 | 0.0729 | - |
| 0.0035 | 500 | 0.0504 | - |
| 0.0042 | 600 | 0.0386 | - |
| 0.0049 | 700 | 0.0314 | - |
| 0.0055 | 800 | 0.0265 | - |
| 0.0062 | 900 | 0.0235 | - |
| 0.0069 | 1000 | 0.0203 | - |
| 0.0076 | 1100 | 0.0186 | - |
| 0.0083 | 1200 | 0.017 | - |
| 0.0090 | 1300 | 0.0157 | - |
| 0.0097 | 1400 | 0.0149 | - |
| 0.0104 | 1500 | 0.0144 | - |
| 0.0111 | 1600 | 0.0139 | - |
| 0.0118 | 1700 | 0.013 | - |
| 0.0125 | 1800 | 0.0125 | - |
| 0.0132 | 1900 | 0.0126 | - |
| 0.0139 | 2000 | 0.0119 | - |
| 0.0146 | 2100 | 0.0116 | - |
| 0.0152 | 2200 | 0.011 | - |
| 0.0159 | 2300 | 0.011 | - |
| 0.0166 | 2400 | 0.0105 | - |
| 0.0173 | 2500 | 0.0104 | - |
| 0.0180 | 2600 | 0.0098 | - |
| 0.0187 | 2700 | 0.0101 | - |
| 0.0194 | 2800 | 0.0094 | - |
| 0.0201 | 2900 | 0.0092 | - |
| 0.0208 | 3000 | 0.0093 | - |
| 0.0215 | 3100 | 0.0089 | - |
| 0.0222 | 3200 | 0.0089 | - |
| 0.0229 | 3300 | 0.0088 | - |
| 0.0236 | 3400 | 0.0086 | - |
| 0.0243 | 3500 | 0.0085 | - |
| 0.0250 | 3600 | 0.0084 | - |
| 0.0256 | 3700 | 0.0081 | - |
| 0.0263 | 3800 | 0.0082 | - |
| 0.0270 | 3900 | 0.0078 | - |
| 0.0277 | 4000 | 0.0079 | - |
| 0.0284 | 4100 | 0.0077 | - |
| 0.0291 | 4200 | 0.0077 | - |
| 0.0298 | 4300 | 0.0076 | - |
| 0.0305 | 4400 | 0.007 | - |
| 0.0312 | 4500 | 0.0072 | - |
| 0.0319 | 4600 | 0.0074 | - |
| 0.0326 | 4700 | 0.0068 | - |
| 0.0333 | 4800 | 0.0072 | - |
| 0.0340 | 4900 | 0.0069 | - |
| 0.0347 | 5000 | 0.007 | - |
| 0.0354 | 5100 | 0.0067 | - |
| 0.0360 | 5200 | 0.0069 | - |
| 0.0367 | 5300 | 0.0069 | - |
| 0.0374 | 5400 | 0.0068 | - |
| 0.0381 | 5500 | 0.0065 | - |
| 0.0388 | 5600 | 0.0063 | - |
| 0.0395 | 5700 | 0.0062 | - |
| 0.0402 | 5800 | 0.0065 | - |
| 0.0409 | 5900 | 0.0062 | - |
| 0.0416 | 6000 | 0.0062 | - |
| 0.0423 | 6100 | 0.0061 | - |
| 0.0430 | 6200 | 0.0062 | - |
| 0.0437 | 6300 | 0.006 | - |
| 0.0444 | 6400 | 0.0061 | - |
| 0.0451 | 6500 | 0.0061 | - |
| 0.0457 | 6600 | 0.0061 | - |
| 0.0464 | 6700 | 0.006 | - |
| 0.0471 | 6800 | 0.006 | - |
| 0.0478 | 6900 | 0.0058 | - |
| 0.0485 | 7000 | 0.0056 | - |
| 0.0492 | 7100 | 0.0056 | - |
| 0.0499 | 7200 | 0.0057 | - |
| 0.0506 | 7300 | 0.0057 | - |
| 0.0513 | 7400 | 0.0055 | - |
| 0.0520 | 7500 | 0.0054 | - |
| 0.0527 | 7600 | 0.0055 | - |
| 0.0534 | 7700 | 0.0055 | - |
| 0.0541 | 7800 | 0.0054 | - |
| 0.0548 | 7900 | 0.0055 | - |
| 0.0555 | 8000 | 0.0055 | - |
| 0.0561 | 8100 | 0.0055 | - |
| 0.0568 | 8200 | 0.0053 | - |
| 0.0575 | 8300 | 0.0054 | - |
| 0.0582 | 8400 | 0.0053 | - |
| 0.0589 | 8500 | 0.005 | - |
| 0.0596 | 8600 | 0.0051 | - |
| 0.0603 | 8700 | 0.0061 | - |
| 0.0610 | 8800 | 0.0058 | - |
| 0.0617 | 8900 | 0.0052 | - |
| 0.0624 | 9000 | 0.0049 | - |
| 0.0631 | 9100 | 0.0051 | - |
| 0.0638 | 9200 | 0.0051 | - |
| 0.0645 | 9300 | 0.005 | - |
| 0.0652 | 9400 | 0.0049 | - |
| 0.0658 | 9500 | 0.0048 | - |
| 0.0665 | 9600 | 0.0048 | - |
| 0.0672 | 9700 | 0.0045 | - |
| 0.0679 | 9800 | 0.0049 | - |
| 0.0686 | 9900 | 0.0049 | - |
| 0.0693 | 10000 | 0.0048 | - |
| 0.0700 | 10100 | 0.0052 | - |
| 0.0707 | 10200 | 0.0051 | - |
| 0.0714 | 10300 | 0.005 | - |
| 0.0721 | 10400 | 0.0048 | - |
| 0.0728 | 10500 | 0.0047 | - |
| 0.0735 | 10600 | 0.0046 | - |
| 0.0742 | 10700 | 0.0046 | - |
| 0.0749 | 10800 | 0.0046 | - |
| 0.0756 | 10900 | 0.0045 | - |
| 0.0762 | 11000 | 0.0045 | - |
| 0.0769 | 11100 | 0.0046 | - |
| 0.0776 | 11200 | 0.0046 | - |
| 0.0783 | 11300 | 0.0045 | - |
| 0.0790 | 11400 | 0.0046 | - |
| 0.0797 | 11500 | 0.0045 | - |
| 0.0804 | 11600 | 0.0044 | - |
| 0.0811 | 11700 | 0.0045 | - |
| 0.0818 | 11800 | 0.0044 | - |
| 0.0825 | 11900 | 0.0045 | - |
| 0.0832 | 12000 | 0.0044 | - |
| 0.0839 | 12100 | 0.0042 | - |
| 0.0846 | 12200 | 0.0042 | - |
| 0.0853 | 12300 | 0.0042 | - |
| 0.0859 | 12400 | 0.0042 | - |
| 0.0866 | 12500 | 0.0042 | - |
| 0.0873 | 12600 | 0.0045 | - |
| 0.0880 | 12700 | 0.0042 | - |
| 0.0887 | 12800 | 0.0043 | - |
| 0.0894 | 12900 | 0.0043 | - |
| 0.0901 | 13000 | 0.0042 | - |
| 0.0908 | 13100 | 0.0043 | - |
| 0.0915 | 13200 | 0.0043 | - |
| 0.0922 | 13300 | 0.0041 | - |
| 0.0929 | 13400 | 0.0041 | - |
| 0.0936 | 13500 | 0.0042 | - |
| 0.0943 | 13600 | 0.004 | - |
| 0.0950 | 13700 | 0.0043 | - |
| 0.0957 | 13800 | 0.004 | - |
| 0.0963 | 13900 | 0.0049 | - |
| 0.0970 | 14000 | 0.0048 | - |
| 0.0977 | 14100 | 0.0044 | - |
| 0.0984 | 14200 | 0.0045 | - |
| 0.0991 | 14300 | 0.0044 | - |
| 0.0998 | 14400 | 0.0042 | - |
| 0.1005 | 14500 | 0.004 | - |
| 0.1012 | 14600 | 0.0039 | - |
| 0.1019 | 14700 | 0.0039 | - |
| 0.1026 | 14800 | 0.0042 | - |
| 0.1033 | 14900 | 0.0039 | - |
| 0.1040 | 15000 | 0.004 | - |
| 0.1047 | 15100 | 0.0038 | - |
| 0.1054 | 15200 | 0.0039 | - |
| 0.1061 | 15300 | 0.0038 | - |
| 0.1067 | 15400 | 0.0039 | - |
| 0.1074 | 15500 | 0.0039 | - |
| 0.1081 | 15600 | 0.0039 | - |
| 0.1088 | 15700 | 0.004 | - |
| 0.1095 | 15800 | 0.0039 | - |
| 0.1102 | 15900 | 0.0039 | - |
| 0.1109 | 16000 | 0.0039 | - |
| 0.1116 | 16100 | 0.0038 | - |
| 0.1123 | 16200 | 0.0039 | - |
| 0.1130 | 16300 | 0.0037 | - |
| 0.1137 | 16400 | 0.0037 | - |
| 0.1144 | 16500 | 0.0038 | - |
| 0.1151 | 16600 | 0.0038 | - |
| 0.1158 | 16700 | 0.0039 | - |
| 0.1164 | 16800 | 0.0037 | - |
| 0.1171 | 16900 | 0.0038 | - |
| 0.1178 | 17000 | 0.0037 | - |
| 0.1185 | 17100 | 0.0037 | - |
| 0.1192 | 17200 | 0.0038 | - |
| 0.1199 | 17300 | 0.0035 | - |
| 0.1206 | 17400 | 0.0038 | - |
| 0.1213 | 17500 | 0.0036 | - |
| 0.1220 | 17600 | 0.0036 | - |
| 0.1227 | 17700 | 0.0038 | - |
| 0.1234 | 17800 | 0.0036 | - |
| 0.1241 | 17900 | 0.0037 | - |
| 0.1248 | 18000 | 0.0037 | - |
| 0.1255 | 18100 | 0.0038 | - |
| 0.1262 | 18200 | 0.0037 | - |
| 0.1268 | 18300 | 0.0036 | - |
| 0.1275 | 18400 | 0.0038 | - |
| 0.1282 | 18500 | 0.0035 | - |
| 0.1289 | 18600 | 0.0037 | - |
| 0.1296 | 18700 | 0.0036 | - |
| 0.1303 | 18800 | 0.0037 | - |
| 0.1310 | 18900 | 0.0036 | - |
| 0.1317 | 19000 | 0.0036 | - |
| 0.1324 | 19100 | 0.0034 | - |
| 0.1331 | 19200 | 0.0035 | - |
| 0.1338 | 19300 | 0.0037 | - |
| 0.1345 | 19400 | 0.0036 | - |
| 0.1352 | 19500 | 0.0035 | - |
| 0.1359 | 19600 | 0.0036 | - |
| 0.1365 | 19700 | 0.0036 | - |
| 0.1372 | 19800 | 0.0035 | - |
| 0.1379 | 19900 | 0.0035 | - |
| 0.1386 | 20000 | 0.0034 | - |
| 0.1393 | 20100 | 0.0034 | - |
| 0.1400 | 20200 | 0.0034 | - |
| 0.1407 | 20300 | 0.0036 | - |
| 0.1414 | 20400 | 0.0034 | - |
| 0.1421 | 20500 | 0.0034 | - |
| 0.1428 | 20600 | 0.0035 | - |
| 0.1435 | 20700 | 0.0034 | - |
| 0.1442 | 20800 | 0.0032 | - |
| 0.1449 | 20900 | 0.0032 | - |
| 0.1456 | 21000 | 0.0034 | - |
| 0.1463 | 21100 | 0.0034 | - |
| 0.1469 | 21200 | 0.0033 | - |
| 0.1476 | 21300 | 0.0034 | - |
| 0.1483 | 21400 | 0.0032 | - |
| 0.1490 | 21500 | 0.0032 | - |
| 0.1497 | 21600 | 0.0034 | - |
| 0.1504 | 21700 | 0.0034 | - |
| 0.1511 | 21800 | 0.0033 | - |
| 0.1518 | 21900 | 0.0034 | - |
| 0.1525 | 22000 | 0.0034 | - |
| 0.1532 | 22100 | 0.0034 | - |
| 0.1539 | 22200 | 0.0033 | - |
| 0.1546 | 22300 | 0.0034 | - |
| 0.1553 | 22400 | 0.0033 | - |
| 0.1560 | 22500 | 0.0031 | - |
| 0.1567 | 22600 | 0.0034 | - |
| 0.1573 | 22700 | 0.0035 | - |
| 0.1580 | 22800 | 0.0033 | - |
| 0.1587 | 22900 | 0.0032 | - |
| 0.1594 | 23000 | 0.0033 | - |
| 0.1601 | 23100 | 0.0032 | - |
| 0.1608 | 23200 | 0.0032 | - |
| 0.1615 | 23300 | 0.0033 | - |
| 0.1622 | 23400 | 0.0032 | - |
| 0.1629 | 23500 | 0.0031 | - |
| 0.1636 | 23600 | 0.003 | - |
| 0.1643 | 23700 | 0.0032 | - |
| 0.1650 | 23800 | 0.0033 | - |
| 0.1657 | 23900 | 0.0032 | - |
| 0.1664 | 24000 | 0.0031 | - |
| 0.1670 | 24100 | 0.0032 | - |
| 0.1677 | 24200 | 0.0032 | - |
| 0.1684 | 24300 | 0.0033 | - |
| 0.1691 | 24400 | 0.0032 | - |
| 0.1698 | 24500 | 0.0033 | - |
| 0.1705 | 24600 | 0.0032 | - |
| 0.1712 | 24700 | 0.0032 | - |
| 0.1719 | 24800 | 0.0033 | - |
| 0.1726 | 24900 | 0.0031 | - |
| 0.1733 | 25000 | 0.0032 | - |
| 0.1740 | 25100 | 0.003 | - |
| 0.1747 | 25200 | 0.0031 | - |
| 0.1754 | 25300 | 0.003 | - |
| 0.1761 | 25400 | 0.003 | - |
| 0.1768 | 25500 | 0.0032 | - |
| 0.1774 | 25600 | 0.003 | - |
| 0.1781 | 25700 | 0.0031 | - |
| 0.1788 | 25800 | 0.0031 | - |
| 0.1795 | 25900 | 0.003 | - |
| 0.1802 | 26000 | 0.003 | - |
| 0.1809 | 26100 | 0.003 | - |
| 0.1816 | 26200 | 0.0031 | - |
| 0.1823 | 26300 | 0.0031 | - |
| 0.1830 | 26400 | 0.0031 | - |
| 0.1837 | 26500 | 0.0032 | - |
| 0.1844 | 26600 | 0.0029 | - |
| 0.1851 | 26700 | 0.0031 | - |
| 0.1858 | 26800 | 0.0031 | - |
| 0.1865 | 26900 | 0.003 | - |
| 0.1871 | 27000 | 0.003 | - |
| 0.1878 | 27100 | 0.003 | - |
| 0.1885 | 27200 | 0.0029 | - |
| 0.1892 | 27300 | 0.003 | - |
| 0.1899 | 27400 | 0.003 | - |
| 0.1906 | 27500 | 0.0029 | - |
| 0.1913 | 27600 | 0.0029 | - |
| 0.1920 | 27700 | 0.0029 | - |
| 0.1927 | 27800 | 0.003 | - |
| 0.1934 | 27900 | 0.003 | - |
| 0.1941 | 28000 | 0.0029 | - |
| 0.1948 | 28100 | 0.003 | - |
| 0.1955 | 28200 | 0.0029 | - |
| 0.1962 | 28300 | 0.0029 | - |
| 0.1969 | 28400 | 0.0029 | - |
| 0.1975 | 28500 | 0.0029 | - |
| 0.1982 | 28600 | 0.0028 | - |
| 0.1989 | 28700 | 0.0029 | - |
| 0.1996 | 28800 | 0.003 | - |
| 0.2 | 28854 | - | 0.0020 |
| 0.2003 | 28900 | 0.003 | - |
| 0.2010 | 29000 | 0.0031 | - |
| 0.2017 | 29100 | 0.003 | - |
| 0.2024 | 29200 | 0.003 | - |
| 0.2031 | 29300 | 0.0031 | - |
| 0.2038 | 29400 | 0.0031 | - |
| 0.2045 | 29500 | 0.003 | - |
| 0.2052 | 29600 | 0.003 | - |
| 0.2059 | 29700 | 0.0031 | - |
| 0.2066 | 29800 | 0.0029 | - |
| 0.2073 | 29900 | 0.003 | - |
| 0.2079 | 30000 | 0.0029 | - |
| 0.2086 | 30100 | 0.0029 | - |
| 0.2093 | 30200 | 0.0031 | - |
| 0.2100 | 30300 | 0.0029 | - |
| 0.2107 | 30400 | 0.0027 | - |
| 0.2114 | 30500 | 0.0029 | - |
| 0.2121 | 30600 | 0.003 | - |
| 0.2128 | 30700 | 0.0029 | - |
| 0.2135 | 30800 | 0.0029 | - |
| 0.2142 | 30900 | 0.003 | - |
| 0.2149 | 31000 | 0.0028 | - |
| 0.2156 | 31100 | 0.0028 | - |
| 0.2163 | 31200 | 0.0029 | - |
| 0.2170 | 31300 | 0.0027 | - |
| 0.2176 | 31400 | 0.0028 | - |
| 0.2183 | 31500 | 0.0027 | - |
| 0.2190 | 31600 | 0.0027 | - |
| 0.2197 | 31700 | 0.0028 | - |
| 0.2204 | 31800 | 0.0027 | - |
| 0.2211 | 31900 | 0.0029 | - |
| 0.2218 | 32000 | 0.0027 | - |
| 0.2225 | 32100 | 0.0028 | - |
| 0.2232 | 32200 | 0.0028 | - |
| 0.2239 | 32300 | 0.0029 | - |
| 0.2246 | 32400 | 0.0027 | - |
| 0.2253 | 32500 | 0.0028 | - |
| 0.2260 | 32600 | 0.0026 | - |
| 0.2267 | 32700 | 0.0028 | - |
| 0.2274 | 32800 | 0.0027 | - |
| 0.2280 | 32900 | 0.0028 | - |
| 0.2287 | 33000 | 0.0026 | - |
| 0.2294 | 33100 | 0.0026 | - |
| 0.2301 | 33200 | 0.0026 | - |
| 0.2308 | 33300 | 0.0028 | - |
| 0.2315 | 33400 | 0.0027 | - |
| 0.2322 | 33500 | 0.0028 | - |
| 0.2329 | 33600 | 0.0028 | - |
| 0.2336 | 33700 | 0.0026 | - |
| 0.2343 | 33800 | 0.0028 | - |
| 0.2350 | 33900 | 0.0027 | - |
| 0.2357 | 34000 | 0.0027 | - |
| 0.2364 | 34100 | 0.0028 | - |
| 0.2371 | 34200 | 0.0029 | - |
| 0.2377 | 34300 | 0.0027 | - |
| 0.2384 | 34400 | 0.0028 | - |
| 0.2391 | 34500 | 0.0027 | - |
| 0.2398 | 34600 | 0.0026 | - |
| 0.2405 | 34700 | 0.0026 | - |
| 0.2412 | 34800 | 0.0026 | - |
| 0.2419 | 34900 | 0.0028 | - |
| 0.2426 | 35000 | 0.0026 | - |
| 0.2433 | 35100 | 0.0026 | - |
| 0.2440 | 35200 | 0.0027 | - |
| 0.2447 | 35300 | 0.0026 | - |
| 0.2454 | 35400 | 0.0026 | - |
| 0.2461 | 35500 | 0.0026 | - |
| 0.2468 | 35600 | 0.0027 | - |
| 0.2475 | 35700 | 0.0028 | - |
| 0.2481 | 35800 | 0.0026 | - |
| 0.2488 | 35900 | 0.0027 | - |
| 0.2495 | 36000 | 0.0027 | - |
| 0.2502 | 36100 | 0.0027 | - |
| 0.2509 | 36200 | 0.0027 | - |
| 0.2516 | 36300 | 0.0026 | - |
| 0.2523 | 36400 | 0.0025 | - |
| 0.2530 | 36500 | 0.0027 | - |
| 0.2537 | 36600 | 0.0028 | - |
| 0.2544 | 36700 | 0.0026 | - |
| 0.2551 | 36800 | 0.0027 | - |
| 0.2558 | 36900 | 0.0026 | - |
| 0.2565 | 37000 | 0.0026 | - |
| 0.2572 | 37100 | 0.0025 | - |
| 0.2578 | 37200 | 0.0026 | - |
| 0.2585 | 37300 | 0.0027 | - |
| 0.2592 | 37400 | 0.0026 | - |
| 0.2599 | 37500 | 0.0026 | - |
| 0.2606 | 37600 | 0.0026 | - |
| 0.2613 | 37700 | 0.0026 | - |
| 0.2620 | 37800 | 0.0025 | - |
| 0.2627 | 37900 | 0.0026 | - |
| 0.2634 | 38000 | 0.0025 | - |
| 0.2641 | 38100 | 0.0025 | - |
| 0.2648 | 38200 | 0.0026 | - |
| 0.2655 | 38300 | 0.0025 | - |
| 0.2662 | 38400 | 0.0025 | - |
| 0.2669 | 38500 | 0.0025 | - |
| 0.2676 | 38600 | 0.0026 | - |
| 0.2682 | 38700 | 0.0026 | - |
| 0.2689 | 38800 | 0.0025 | - |
| 0.2696 | 38900 | 0.0026 | - |
| 0.2703 | 39000 | 0.0026 | - |
| 0.2710 | 39100 | 0.0026 | - |
| 0.2717 | 39200 | 0.0025 | - |
| 0.2724 | 39300 | 0.0026 | - |
| 0.2731 | 39400 | 0.0026 | - |
| 0.2738 | 39500 | 0.0027 | - |
| 0.2745 | 39600 | 0.0025 | - |
| 0.2752 | 39700 | 0.0026 | - |
| 0.2759 | 39800 | 0.0025 | - |
| 0.2766 | 39900 | 0.0026 | - |
| 0.2773 | 40000 | 0.0027 | - |
| 0.2780 | 40100 | 0.0025 | - |
| 0.2786 | 40200 | 0.0026 | - |
| 0.2793 | 40300 | 0.0026 | - |
| 0.2800 | 40400 | 0.0026 | - |
| 0.2807 | 40500 | 0.0024 | - |
| 0.2814 | 40600 | 0.0025 | - |
| 0.2821 | 40700 | 0.0027 | - |
| 0.2828 | 40800 | 0.0024 | - |
| 0.2835 | 40900 | 0.0025 | - |
| 0.2842 | 41000 | 0.0025 | - |
| 0.2849 | 41100 | 0.0024 | - |
| 0.2856 | 41200 | 0.0025 | - |
| 0.2863 | 41300 | 0.0025 | - |
| 0.2870 | 41400 | 0.0024 | - |
| 0.2877 | 41500 | 0.0026 | - |
| 0.2883 | 41600 | 0.0024 | - |
| 0.2890 | 41700 | 0.0026 | - |
| 0.2897 | 41800 | 0.0027 | - |
| 0.2904 | 41900 | 0.0025 | - |
| 0.2911 | 42000 | 0.0024 | - |
| 0.2918 | 42100 | 0.0026 | - |
| 0.2925 | 42200 | 0.0025 | - |
| 0.2932 | 42300 | 0.0026 | - |
| 0.2939 | 42400 | 0.0025 | - |
| 0.2946 | 42500 | 0.0025 | - |
| 0.2953 | 42600 | 0.0025 | - |
| 0.2960 | 42700 | 0.0023 | - |
| 0.2967 | 42800 | 0.0025 | - |
| 0.2974 | 42900 | 0.0023 | - |
| 0.2981 | 43000 | 0.0024 | - |
| 0.2987 | 43100 | 0.0025 | - |
| 0.2994 | 43200 | 0.0024 | - |
| 0.3001 | 43300 | 0.0024 | - |
| 0.3008 | 43400 | 0.0025 | - |
| 0.3015 | 43500 | 0.0024 | - |
| 0.3022 | 43600 | 0.0024 | - |
| 0.3029 | 43700 | 0.0024 | - |
| 0.3036 | 43800 | 0.0024 | - |
| 0.3043 | 43900 | 0.0023 | - |
| 0.3050 | 44000 | 0.0025 | - |
| 0.3057 | 44100 | 0.0025 | - |
| 0.3064 | 44200 | 0.0024 | - |
| 0.3071 | 44300 | 0.0024 | - |
| 0.3078 | 44400 | 0.0023 | - |
| 0.3084 | 44500 | 0.0025 | - |
| 0.3091 | 44600 | 0.0024 | - |
| 0.3098 | 44700 | 0.0025 | - |
| 0.3105 | 44800 | 0.0025 | - |
| 0.3112 | 44900 | 0.0026 | - |
| 0.3119 | 45000 | 0.0025 | - |
| 0.3126 | 45100 | 0.0025 | - |
| 0.3133 | 45200 | 0.0025 | - |
| 0.3140 | 45300 | 0.0024 | - |
| 0.3147 | 45400 | 0.0023 | - |
| 0.3154 | 45500 | 0.0024 | - |
| 0.3161 | 45600 | 0.0024 | - |
| 0.3168 | 45700 | 0.0024 | - |
| 0.3175 | 45800 | 0.0023 | - |
| 0.3182 | 45900 | 0.0024 | - |
| 0.3188 | 46000 | 0.0024 | - |
| 0.3195 | 46100 | 0.0023 | - |
| 0.3202 | 46200 | 0.0025 | - |
| 0.3209 | 46300 | 0.0024 | - |
| 0.3216 | 46400 | 0.0025 | - |
| 0.3223 | 46500 | 0.0023 | - |
| 0.3230 | 46600 | 0.0024 | - |
| 0.3237 | 46700 | 0.0023 | - |
| 0.3244 | 46800 | 0.0024 | - |
| 0.3251 | 46900 | 0.0023 | - |
| 0.3258 | 47000 | 0.0023 | - |
| 0.3265 | 47100 | 0.0024 | - |
| 0.3272 | 47200 | 0.0023 | - |
| 0.3279 | 47300 | 0.0023 | - |
| 0.3286 | 47400 | 0.0024 | - |
| 0.3292 | 47500 | 0.0023 | - |
| 0.3299 | 47600 | 0.0024 | - |
| 0.3306 | 47700 | 0.0024 | - |
| 0.3313 | 47800 | 0.0023 | - |
| 0.3320 | 47900 | 0.0024 | - |
| 0.3327 | 48000 | 0.0023 | - |
| 0.3334 | 48100 | 0.0023 | - |
| 0.3341 | 48200 | 0.0023 | - |
| 0.3348 | 48300 | 0.0024 | - |
| 0.3355 | 48400 | 0.0023 | - |
| 0.3362 | 48500 | 0.0023 | - |
| 0.3369 | 48600 | 0.0023 | - |
| 0.3376 | 48700 | 0.0022 | - |
| 0.3383 | 48800 | 0.0023 | - |
| 0.3389 | 48900 | 0.0023 | - |
| 0.3396 | 49000 | 0.0023 | - |
| 0.3403 | 49100 | 0.0023 | - |
| 0.3410 | 49200 | 0.0022 | - |
| 0.3417 | 49300 | 0.0022 | - |
| 0.3424 | 49400 | 0.0024 | - |
| 0.3431 | 49500 | 0.0022 | - |
| 0.3438 | 49600 | 0.0024 | - |
| 0.3445 | 49700 | 0.0024 | - |
| 0.3452 | 49800 | 0.0022 | - |
| 0.3459 | 49900 | 0.0023 | - |
| 0.3466 | 50000 | 0.0023 | - |
| 0.3473 | 50100 | 0.0024 | - |
| 0.3480 | 50200 | 0.0023 | - |
| 0.3487 | 50300 | 0.0022 | - |
| 0.3493 | 50400 | 0.0023 | - |
| 0.3500 | 50500 | 0.0022 | - |
| 0.3507 | 50600 | 0.0023 | - |
| 0.3514 | 50700 | 0.0023 | - |
| 0.3521 | 50800 | 0.0023 | - |
| 0.3528 | 50900 | 0.0023 | - |
| 0.3535 | 51000 | 0.0023 | - |
| 0.3542 | 51100 | 0.0022 | - |
| 0.3549 | 51200 | 0.0023 | - |
| 0.3556 | 51300 | 0.0022 | - |
| 0.3563 | 51400 | 0.0024 | - |
| 0.3570 | 51500 | 0.0022 | - |
| 0.3577 | 51600 | 0.0022 | - |
| 0.3584 | 51700 | 0.0023 | - |
| 0.3590 | 51800 | 0.0023 | - |
| 0.3597 | 51900 | 0.0023 | - |
| 0.3604 | 52000 | 0.0022 | - |
| 0.3611 | 52100 | 0.0023 | - |
| 0.3618 | 52200 | 0.0022 | - |
| 0.3625 | 52300 | 0.0021 | - |
| 0.3632 | 52400 | 0.0022 | - |
| 0.3639 | 52500 | 0.0022 | - |
| 0.3646 | 52600 | 0.0023 | - |
| 0.3653 | 52700 | 0.0021 | - |
| 0.3660 | 52800 | 0.0023 | - |
| 0.3667 | 52900 | 0.0022 | - |
| 0.3674 | 53000 | 0.0023 | - |
| 0.3681 | 53100 | 0.0022 | - |
| 0.3688 | 53200 | 0.0023 | - |
| 0.3694 | 53300 | 0.0024 | - |
| 0.3701 | 53400 | 0.0023 | - |
| 0.3708 | 53500 | 0.0022 | - |
| 0.3715 | 53600 | 0.0022 | - |
| 0.3722 | 53700 | 0.0023 | - |
| 0.3729 | 53800 | 0.0023 | - |
| 0.3736 | 53900 | 0.0023 | - |
| 0.3743 | 54000 | 0.0022 | - |
| 0.3750 | 54100 | 0.0023 | - |
| 0.3757 | 54200 | 0.0022 | - |
| 0.3764 | 54300 | 0.0023 | - |
| 0.3771 | 54400 | 0.0023 | - |
| 0.3778 | 54500 | 0.0022 | - |
| 0.3785 | 54600 | 0.0022 | - |
| 0.3792 | 54700 | 0.0022 | - |
| 0.3798 | 54800 | 0.0021 | - |
| 0.3805 | 54900 | 0.0023 | - |
| 0.3812 | 55000 | 0.0022 | - |
| 0.3819 | 55100 | 0.0022 | - |
| 0.3826 | 55200 | 0.0023 | - |
| 0.3833 | 55300 | 0.0021 | - |
| 0.3840 | 55400 | 0.0021 | - |
| 0.3847 | 55500 | 0.0022 | - |
| 0.3854 | 55600 | 0.0022 | - |
| 0.3861 | 55700 | 0.0022 | - |
| 0.3868 | 55800 | 0.0022 | - |
| 0.3875 | 55900 | 0.0022 | - |
| 0.3882 | 56000 | 0.0022 | - |
| 0.3889 | 56100 | 0.0022 | - |
| 0.3895 | 56200 | 0.0021 | - |
| 0.3902 | 56300 | 0.0022 | - |
| 0.3909 | 56400 | 0.0021 | - |
| 0.3916 | 56500 | 0.0022 | - |
| 0.3923 | 56600 | 0.0022 | - |
| 0.3930 | 56700 | 0.0024 | - |
| 0.3937 | 56800 | 0.0021 | - |
| 0.3944 | 56900 | 0.0021 | - |
| 0.3951 | 57000 | 0.0022 | - |
| 0.3958 | 57100 | 0.0021 | - |
| 0.3965 | 57200 | 0.0022 | - |
| 0.3972 | 57300 | 0.0022 | - |
| 0.3979 | 57400 | 0.0023 | - |
| 0.3986 | 57500 | 0.0022 | - |
| 0.3993 | 57600 | 0.0022 | - |
| 0.3999 | 57700 | 0.0022 | - |
| 0.4 | 57708 | - | 0.0015 |
| 0.4006 | 57800 | 0.0022 | - |
| 0.4013 | 57900 | 0.0021 | - |
| 0.4020 | 58000 | 0.0022 | - |
| 0.4027 | 58100 | 0.0021 | - |
| 0.4034 | 58200 | 0.0022 | - |
| 0.4041 | 58300 | 0.0022 | - |
| 0.4048 | 58400 | 0.002 | - |
| 0.4055 | 58500 | 0.0023 | - |
| 0.4062 | 58600 | 0.0021 | - |
| 0.4069 | 58700 | 0.0022 | - |
| 0.4076 | 58800 | 0.0021 | - |
| 0.4083 | 58900 | 0.0021 | - |
| 0.4090 | 59000 | 0.0022 | - |
| 0.4096 | 59100 | 0.002 | - |
| 0.4103 | 59200 | 0.0021 | - |
| 0.4110 | 59300 | 0.0022 | - |
| 0.4117 | 59400 | 0.0023 | - |
| 0.4124 | 59500 | 0.0022 | - |
| 0.4131 | 59600 | 0.0021 | - |
| 0.4138 | 59700 | 0.002 | - |
| 0.4145 | 59800 | 0.0021 | - |
| 0.4152 | 59900 | 0.0022 | - |
| 0.4159 | 60000 | 0.0022 | - |
| 0.4166 | 60100 | 0.0023 | - |
| 0.4173 | 60200 | 0.0022 | - |
| 0.4180 | 60300 | 0.0021 | - |
| 0.4187 | 60400 | 0.0021 | - |
| 0.4194 | 60500 | 0.0021 | - |
| 0.4200 | 60600 | 0.0022 | - |
| 0.4207 | 60700 | 0.0022 | - |
| 0.4214 | 60800 | 0.0022 | - |
| 0.4221 | 60900 | 0.0021 | - |
| 0.4228 | 61000 | 0.0022 | - |
| 0.4235 | 61100 | 0.002 | - |
| 0.4242 | 61200 | 0.0022 | - |
| 0.4249 | 61300 | 0.0021 | - |
| 0.4256 | 61400 | 0.0021 | - |
| 0.4263 | 61500 | 0.0021 | - |
| 0.4270 | 61600 | 0.0021 | - |
| 0.4277 | 61700 | 0.0021 | - |
| 0.4284 | 61800 | 0.0022 | - |
| 0.4291 | 61900 | 0.0022 | - |
| 0.4297 | 62000 | 0.0021 | - |
| 0.4304 | 62100 | 0.002 | - |
| 0.4311 | 62200 | 0.002 | - |
| 0.4318 | 62300 | 0.0021 | - |
| 0.4325 | 62400 | 0.0021 | - |
| 0.4332 | 62500 | 0.0021 | - |
| 0.4339 | 62600 | 0.0021 | - |
| 0.4346 | 62700 | 0.0023 | - |
| 0.4353 | 62800 | 0.0021 | - |
| 0.4360 | 62900 | 0.002 | - |
| 0.4367 | 63000 | 0.0019 | - |
| 0.4374 | 63100 | 0.0021 | - |
| 0.4381 | 63200 | 0.0021 | - |
| 0.4388 | 63300 | 0.0021 | - |
| 0.4395 | 63400 | 0.0023 | - |
| 0.4401 | 63500 | 0.0021 | - |
| 0.4408 | 63600 | 0.002 | - |
| 0.4415 | 63700 | 0.0021 | - |
| 0.4422 | 63800 | 0.0022 | - |
| 0.4429 | 63900 | 0.0022 | - |
| 0.4436 | 64000 | 0.0022 | - |
| 0.4443 | 64100 | 0.002 | - |
| 0.4450 | 64200 | 0.0022 | - |
| 0.4457 | 64300 | 0.0021 | - |
| 0.4464 | 64400 | 0.002 | - |
| 0.4471 | 64500 | 0.0021 | - |
| 0.4478 | 64600 | 0.002 | - |
| 0.4485 | 64700 | 0.0021 | - |
| 0.4492 | 64800 | 0.0021 | - |
| 0.4499 | 64900 | 0.0022 | - |
| 0.4505 | 65000 | 0.0021 | - |
| 0.4512 | 65100 | 0.0021 | - |
| 0.4519 | 65200 | 0.0021 | - |
| 0.4526 | 65300 | 0.002 | - |
| 0.4533 | 65400 | 0.0021 | - |
| 0.4540 | 65500 | 0.002 | - |
| 0.4547 | 65600 | 0.002 | - |
| 0.4554 | 65700 | 0.0021 | - |
| 0.4561 | 65800 | 0.002 | - |
| 0.4568 | 65900 | 0.002 | - |
| 0.4575 | 66000 | 0.0021 | - |
| 0.4582 | 66100 | 0.002 | - |
| 0.4589 | 66200 | 0.0021 | - |
| 0.4596 | 66300 | 0.0019 | - |
| 0.4602 | 66400 | 0.002 | - |
| 0.4609 | 66500 | 0.002 | - |
| 0.4616 | 66600 | 0.002 | - |
| 0.4623 | 66700 | 0.002 | - |
| 0.4630 | 66800 | 0.002 | - |
| 0.4637 | 66900 | 0.002 | - |
| 0.4644 | 67000 | 0.002 | - |
| 0.4651 | 67100 | 0.0021 | - |
| 0.4658 | 67200 | 0.002 | - |
| 0.4665 | 67300 | 0.0021 | - |
| 0.4672 | 67400 | 0.002 | - |
| 0.4679 | 67500 | 0.0021 | - |
| 0.4686 | 67600 | 0.0021 | - |
| 0.4693 | 67700 | 0.0022 | - |
| 0.4700 | 67800 | 0.002 | - |
| 0.4706 | 67900 | 0.0021 | - |
| 0.4713 | 68000 | 0.002 | - |
| 0.4720 | 68100 | 0.0021 | - |
| 0.4727 | 68200 | 0.002 | - |
| 0.4734 | 68300 | 0.002 | - |
| 0.4741 | 68400 | 0.0019 | - |
| 0.4748 | 68500 | 0.002 | - |
| 0.4755 | 68600 | 0.002 | - |
| 0.4762 | 68700 | 0.002 | - |
| 0.4769 | 68800 | 0.002 | - |
| 0.4776 | 68900 | 0.0021 | - |
| 0.4783 | 69000 | 0.002 | - |
| 0.4790 | 69100 | 0.002 | - |
| 0.4797 | 69200 | 0.002 | - |
| 0.4803 | 69300 | 0.002 | - |
| 0.4810 | 69400 | 0.002 | - |
| 0.4817 | 69500 | 0.002 | - |
| 0.4824 | 69600 | 0.0021 | - |
| 0.4831 | 69700 | 0.0021 | - |
| 0.4838 | 69800 | 0.002 | - |
| 0.4845 | 69900 | 0.0019 | - |
| 0.4852 | 70000 | 0.0021 | - |
| 0.4859 | 70100 | 0.002 | - |
| 0.4866 | 70200 | 0.0021 | - |
| 0.4873 | 70300 | 0.0019 | - |
| 0.4880 | 70400 | 0.002 | - |
| 0.4887 | 70500 | 0.0021 | - |
| 0.4894 | 70600 | 0.0018 | - |
| 0.4901 | 70700 | 0.002 | - |
| 0.4907 | 70800 | 0.0021 | - |
| 0.4914 | 70900 | 0.0021 | - |
| 0.4921 | 71000 | 0.002 | - |
| 0.4928 | 71100 | 0.002 | - |
| 0.4935 | 71200 | 0.002 | - |
| 0.4942 | 71300 | 0.002 | - |
| 0.4949 | 71400 | 0.002 | - |
| 0.4956 | 71500 | 0.0019 | - |
| 0.4963 | 71600 | 0.002 | - |
| 0.4970 | 71700 | 0.002 | - |
| 0.4977 | 71800 | 0.0021 | - |
| 0.4984 | 71900 | 0.0019 | - |
| 0.4991 | 72000 | 0.002 | - |
| 0.4998 | 72100 | 0.0021 | - |
| 0.5005 | 72200 | 0.0019 | - |
| 0.5011 | 72300 | 0.002 | - |
| 0.5018 | 72400 | 0.0019 | - |
| 0.5025 | 72500 | 0.002 | - |
| 0.5032 | 72600 | 0.002 | - |
| 0.5039 | 72700 | 0.0021 | - |
| 0.5046 | 72800 | 0.002 | - |
| 0.5053 | 72900 | 0.0019 | - |
| 0.5060 | 73000 | 0.002 | - |
| 0.5067 | 73100 | 0.002 | - |
| 0.5074 | 73200 | 0.002 | - |
| 0.5081 | 73300 | 0.0019 | - |
| 0.5088 | 73400 | 0.002 | - |
| 0.5095 | 73500 | 0.002 | - |
| 0.5102 | 73600 | 0.002 | - |
| 0.5108 | 73700 | 0.002 | - |
| 0.5115 | 73800 | 0.0021 | - |
| 0.5122 | 73900 | 0.002 | - |
| 0.5129 | 74000 | 0.002 | - |
| 0.5136 | 74100 | 0.002 | - |
| 0.5143 | 74200 | 0.002 | - |
| 0.5150 | 74300 | 0.0019 | - |
| 0.5157 | 74400 | 0.002 | - |
| 0.5164 | 74500 | 0.002 | - |
| 0.5171 | 74600 | 0.002 | - |
| 0.5178 | 74700 | 0.002 | - |
| 0.5185 | 74800 | 0.002 | - |
| 0.5192 | 74900 | 0.0018 | - |
| 0.5199 | 75000 | 0.0019 | - |
| 0.5206 | 75100 | 0.0019 | - |
| 0.5212 | 75200 | 0.0019 | - |
| 0.5219 | 75300 | 0.002 | - |
| 0.5226 | 75400 | 0.0019 | - |
| 0.5233 | 75500 | 0.0018 | - |
| 0.5240 | 75600 | 0.0018 | - |
| 0.5247 | 75700 | 0.0019 | - |
| 0.5254 | 75800 | 0.002 | - |
| 0.5261 | 75900 | 0.0019 | - |
| 0.5268 | 76000 | 0.002 | - |
| 0.5275 | 76100 | 0.0018 | - |
| 0.5282 | 76200 | 0.0019 | - |
| 0.5289 | 76300 | 0.002 | - |
| 0.5296 | 76400 | 0.0019 | - |
| 0.5303 | 76500 | 0.0019 | - |
| 0.5309 | 76600 | 0.002 | - |
| 0.5316 | 76700 | 0.0019 | - |
| 0.5323 | 76800 | 0.0019 | - |
| 0.5330 | 76900 | 0.0019 | - |
| 0.5337 | 77000 | 0.002 | - |
| 0.5344 | 77100 | 0.0018 | - |
| 0.5351 | 77200 | 0.002 | - |
| 0.5358 | 77300 | 0.0019 | - |
| 0.5365 | 77400 | 0.0019 | - |
| 0.5372 | 77500 | 0.0018 | - |
| 0.5379 | 77600 | 0.0019 | - |
| 0.5386 | 77700 | 0.0019 | - |
| 0.5393 | 77800 | 0.0018 | - |
| 0.5400 | 77900 | 0.002 | - |
| 0.5407 | 78000 | 0.002 | - |
| 0.5413 | 78100 | 0.0018 | - |
| 0.5420 | 78200 | 0.0019 | - |
| 0.5427 | 78300 | 0.0018 | - |
| 0.5434 | 78400 | 0.0019 | - |
| 0.5441 | 78500 | 0.0019 | - |
| 0.5448 | 78600 | 0.0018 | - |
| 0.5455 | 78700 | 0.002 | - |
| 0.5462 | 78800 | 0.0019 | - |
| 0.5469 | 78900 | 0.0019 | - |
| 0.5476 | 79000 | 0.0019 | - |
| 0.5483 | 79100 | 0.0018 | - |
| 0.5490 | 79200 | 0.0019 | - |
| 0.5497 | 79300 | 0.0019 | - |
| 0.5504 | 79400 | 0.0019 | - |
| 0.5511 | 79500 | 0.0018 | - |
| 0.5517 | 79600 | 0.0019 | - |
| 0.5524 | 79700 | 0.0019 | - |
| 0.5531 | 79800 | 0.0019 | - |
| 0.5538 | 79900 | 0.0019 | - |
| 0.5545 | 80000 | 0.0019 | - |
| 0.5552 | 80100 | 0.0018 | - |
| 0.5559 | 80200 | 0.0019 | - |
| 0.5566 | 80300 | 0.0019 | - |
| 0.5573 | 80400 | 0.0018 | - |
| 0.5580 | 80500 | 0.0018 | - |
| 0.5587 | 80600 | 0.0018 | - |
| 0.5594 | 80700 | 0.0018 | - |
| 0.5601 | 80800 | 0.0019 | - |
| 0.5608 | 80900 | 0.0019 | - |
| 0.5614 | 81000 | 0.0019 | - |
| 0.5621 | 81100 | 0.0019 | - |
| 0.5628 | 81200 | 0.0019 | - |
| 0.5635 | 81300 | 0.002 | - |
| 0.5642 | 81400 | 0.0019 | - |
| 0.5649 | 81500 | 0.0019 | - |
| 0.5656 | 81600 | 0.0019 | - |
| 0.5663 | 81700 | 0.0018 | - |
| 0.5670 | 81800 | 0.0019 | - |
| 0.5677 | 81900 | 0.0018 | - |
| 0.5684 | 82000 | 0.0019 | - |
| 0.5691 | 82100 | 0.0021 | - |
| 0.5698 | 82200 | 0.0019 | - |
| 0.5705 | 82300 | 0.0019 | - |
| 0.5712 | 82400 | 0.0017 | - |
| 0.5718 | 82500 | 0.0019 | - |
| 0.5725 | 82600 | 0.002 | - |
| 0.5732 | 82700 | 0.0019 | - |
| 0.5739 | 82800 | 0.0019 | - |
| 0.5746 | 82900 | 0.0019 | - |
| 0.5753 | 83000 | 0.0018 | - |
| 0.5760 | 83100 | 0.0019 | - |
| 0.5767 | 83200 | 0.0019 | - |
| 0.5774 | 83300 | 0.0019 | - |
| 0.5781 | 83400 | 0.0018 | - |
| 0.5788 | 83500 | 0.0019 | - |
| 0.5795 | 83600 | 0.0019 | - |
| 0.5802 | 83700 | 0.0018 | - |
| 0.5809 | 83800 | 0.0019 | - |
| 0.5815 | 83900 | 0.0019 | - |
| 0.5822 | 84000 | 0.0019 | - |
| 0.5829 | 84100 | 0.0018 | - |
| 0.5836 | 84200 | 0.0018 | - |
| 0.5843 | 84300 | 0.0017 | - |
| 0.5850 | 84400 | 0.0018 | - |
| 0.5857 | 84500 | 0.0018 | - |
| 0.5864 | 84600 | 0.0018 | - |
| 0.5871 | 84700 | 0.0017 | - |
| 0.5878 | 84800 | 0.0018 | - |
| 0.5885 | 84900 | 0.0019 | - |
| 0.5892 | 85000 | 0.002 | - |
| 0.5899 | 85100 | 0.0018 | - |
| 0.5906 | 85200 | 0.0018 | - |
| 0.5913 | 85300 | 0.0019 | - |
| 0.5919 | 85400 | 0.0018 | - |
| 0.5926 | 85500 | 0.0019 | - |
| 0.5933 | 85600 | 0.0019 | - |
| 0.5940 | 85700 | 0.0019 | - |
| 0.5947 | 85800 | 0.0019 | - |
| 0.5954 | 85900 | 0.0019 | - |
| 0.5961 | 86000 | 0.0019 | - |
| 0.5968 | 86100 | 0.0018 | - |
| 0.5975 | 86200 | 0.0018 | - |
| 0.5982 | 86300 | 0.0019 | - |
| 0.5989 | 86400 | 0.0018 | - |
| 0.5996 | 86500 | 0.0018 | - |
| 0.6 | 86562 | - | 0.0012 |
| 0.6003 | 86600 | 0.0018 | - |
| 0.6010 | 86700 | 0.0018 | - |
| 0.6016 | 86800 | 0.0018 | - |
| 0.6023 | 86900 | 0.0018 | - |
| 0.6030 | 87000 | 0.0018 | - |
| 0.6037 | 87100 | 0.0017 | - |
| 0.6044 | 87200 | 0.0019 | - |
| 0.6051 | 87300 | 0.0018 | - |
| 0.6058 | 87400 | 0.0018 | - |
| 0.6065 | 87500 | 0.0017 | - |
| 0.6072 | 87600 | 0.0018 | - |
| 0.6079 | 87700 | 0.0019 | - |
| 0.6086 | 87800 | 0.0018 | - |
| 0.6093 | 87900 | 0.0018 | - |
| 0.6100 | 88000 | 0.0018 | - |
| 0.6107 | 88100 | 0.0019 | - |
| 0.6114 | 88200 | 0.0018 | - |
| 0.6120 | 88300 | 0.0017 | - |
| 0.6127 | 88400 | 0.0018 | - |
| 0.6134 | 88500 | 0.0018 | - |
| 0.6141 | 88600 | 0.0018 | - |
| 0.6148 | 88700 | 0.0019 | - |
| 0.6155 | 88800 | 0.0018 | - |
| 0.6162 | 88900 | 0.0018 | - |
| 0.6169 | 89000 | 0.0018 | - |
| 0.6176 | 89100 | 0.0017 | - |
| 0.6183 | 89200 | 0.0018 | - |
| 0.6190 | 89300 | 0.0017 | - |
| 0.6197 | 89400 | 0.0019 | - |
| 0.6204 | 89500 | 0.0018 | - |
| 0.6211 | 89600 | 0.0019 | - |
| 0.6218 | 89700 | 0.0017 | - |
| 0.6224 | 89800 | 0.0018 | - |
| 0.6231 | 89900 | 0.0018 | - |
| 0.6238 | 90000 | 0.0018 | - |
| 0.6245 | 90100 | 0.0019 | - |
| 0.6252 | 90200 | 0.0017 | - |
| 0.6259 | 90300 | 0.0018 | - |
| 0.6266 | 90400 | 0.0018 | - |
| 0.6273 | 90500 | 0.0017 | - |
| 0.6280 | 90600 | 0.0018 | - |
| 0.6287 | 90700 | 0.0018 | - |
| 0.6294 | 90800 | 0.0018 | - |
| 0.6301 | 90900 | 0.0018 | - |
| 0.6308 | 91000 | 0.0018 | - |
| 0.6315 | 91100 | 0.0017 | - |
| 0.6321 | 91200 | 0.0018 | - |
| 0.6328 | 91300 | 0.0018 | - |
| 0.6335 | 91400 | 0.0018 | - |
| 0.6342 | 91500 | 0.0017 | - |
| 0.6349 | 91600 | 0.0017 | - |
| 0.6356 | 91700 | 0.0017 | - |
| 0.6363 | 91800 | 0.002 | - |
| 0.6370 | 91900 | 0.0018 | - |
| 0.6377 | 92000 | 0.0018 | - |
| 0.6384 | 92100 | 0.0017 | - |
| 0.6391 | 92200 | 0.0017 | - |
| 0.6398 | 92300 | 0.0017 | - |
| 0.6405 | 92400 | 0.0018 | - |
| 0.6412 | 92500 | 0.0018 | - |
| 0.6419 | 92600 | 0.0018 | - |
| 0.6425 | 92700 | 0.0018 | - |
| 0.6432 | 92800 | 0.0018 | - |
| 0.6439 | 92900 | 0.0018 | - |
| 0.6446 | 93000 | 0.0017 | - |
| 0.6453 | 93100 | 0.0019 | - |
| 0.6460 | 93200 | 0.0018 | - |
| 0.6467 | 93300 | 0.0017 | - |
| 0.6474 | 93400 | 0.0018 | - |
| 0.6481 | 93500 | 0.0018 | - |
| 0.6488 | 93600 | 0.0018 | - |
| 0.6495 | 93700 | 0.0019 | - |
| 0.6502 | 93800 | 0.002 | - |
| 0.6509 | 93900 | 0.0018 | - |
| 0.6516 | 94000 | 0.0017 | - |
| 0.6522 | 94100 | 0.0017 | - |
| 0.6529 | 94200 | 0.0018 | - |
| 0.6536 | 94300 | 0.0018 | - |
| 0.6543 | 94400 | 0.0017 | - |
| 0.6550 | 94500 | 0.0018 | - |
| 0.6557 | 94600 | 0.0016 | - |
| 0.6564 | 94700 | 0.0018 | - |
| 0.6571 | 94800 | 0.0018 | - |
| 0.6578 | 94900 | 0.0019 | - |
| 0.6585 | 95000 | 0.0018 | - |
| 0.6592 | 95100 | 0.0017 | - |
| 0.6599 | 95200 | 0.0017 | - |
| 0.6606 | 95300 | 0.0018 | - |
| 0.6613 | 95400 | 0.0017 | - |
| 0.6620 | 95500 | 0.0018 | - |
| 0.6626 | 95600 | 0.0019 | - |
| 0.6633 | 95700 | 0.0017 | - |
| 0.6640 | 95800 | 0.0019 | - |
| 0.6647 | 95900 | 0.0017 | - |
| 0.6654 | 96000 | 0.0018 | - |
| 0.6661 | 96100 | 0.0018 | - |
| 0.6668 | 96200 | 0.0019 | - |
| 0.6675 | 96300 | 0.0018 | - |
| 0.6682 | 96400 | 0.0017 | - |
| 0.6689 | 96500 | 0.0017 | - |
| 0.6696 | 96600 | 0.0017 | - |
| 0.6703 | 96700 | 0.0017 | - |
| 0.6710 | 96800 | 0.0017 | - |
| 0.6717 | 96900 | 0.0018 | - |
| 0.6724 | 97000 | 0.0018 | - |
| 0.6730 | 97100 | 0.0017 | - |
| 0.6737 | 97200 | 0.0018 | - |
| 0.6744 | 97300 | 0.0017 | - |
| 0.6751 | 97400 | 0.0017 | - |
| 0.6758 | 97500 | 0.0017 | - |
| 0.6765 | 97600 | 0.0017 | - |
| 0.6772 | 97700 | 0.0018 | - |
| 0.6779 | 97800 | 0.0017 | - |
| 0.6786 | 97900 | 0.0018 | - |
| 0.6793 | 98000 | 0.0018 | - |
| 0.6800 | 98100 | 0.0018 | - |
| 0.6807 | 98200 | 0.0018 | - |
| 0.6814 | 98300 | 0.0017 | - |
| 0.6821 | 98400 | 0.0018 | - |
| 0.6827 | 98500 | 0.0018 | - |
| 0.6834 | 98600 | 0.0017 | - |
| 0.6841 | 98700 | 0.0018 | - |
| 0.6848 | 98800 | 0.0017 | - |
| 0.6855 | 98900 | 0.0018 | - |
| 0.6862 | 99000 | 0.0018 | - |
| 0.6869 | 99100 | 0.0017 | - |
| 0.6876 | 99200 | 0.0017 | - |
| 0.6883 | 99300 | 0.0017 | - |
| 0.6890 | 99400 | 0.0017 | - |
| 0.6897 | 99500 | 0.0016 | - |
| 0.6904 | 99600 | 0.0018 | - |
| 0.6911 | 99700 | 0.0017 | - |
| 0.6918 | 99800 | 0.0016 | - |
| 0.6925 | 99900 | 0.0017 | - |
| 0.6931 | 100000 | 0.0017 | - |
| 0.6938 | 100100 | 0.0018 | - |
| 0.6945 | 100200 | 0.0019 | - |
| 0.6952 | 100300 | 0.0017 | - |
| 0.6959 | 100400 | 0.0017 | - |
| 0.6966 | 100500 | 0.0017 | - |
| 0.6973 | 100600 | 0.0018 | - |
| 0.6980 | 100700 | 0.0016 | - |
| 0.6987 | 100800 | 0.0018 | - |
| 0.6994 | 100900 | 0.0017 | - |
| 0.7001 | 101000 | 0.0016 | - |
| 0.7008 | 101100 | 0.0017 | - |
| 0.7015 | 101200 | 0.0016 | - |
| 0.7022 | 101300 | 0.0017 | - |
| 0.7028 | 101400 | 0.0017 | - |
| 0.7035 | 101500 | 0.0017 | - |
| 0.7042 | 101600 | 0.0017 | - |
| 0.7049 | 101700 | 0.0017 | - |
| 0.7056 | 101800 | 0.0017 | - |
| 0.7063 | 101900 | 0.0017 | - |
| 0.7070 | 102000 | 0.0017 | - |
| 0.7077 | 102100 | 0.0018 | - |
| 0.7084 | 102200 | 0.0017 | - |
| 0.7091 | 102300 | 0.0018 | - |
| 0.7098 | 102400 | 0.0017 | - |
| 0.7105 | 102500 | 0.0018 | - |
| 0.7112 | 102600 | 0.0017 | - |
| 0.7119 | 102700 | 0.0017 | - |
| 0.7126 | 102800 | 0.0017 | - |
| 0.7132 | 102900 | 0.0017 | - |
| 0.7139 | 103000 | 0.0017 | - |
| 0.7146 | 103100 | 0.0017 | - |
| 0.7153 | 103200 | 0.0017 | - |
| 0.7160 | 103300 | 0.0016 | - |
| 0.7167 | 103400 | 0.0017 | - |
| 0.7174 | 103500 | 0.0017 | - |
| 0.7181 | 103600 | 0.0016 | - |
| 0.7188 | 103700 | 0.0017 | - |
| 0.7195 | 103800 | 0.0017 | - |
| 0.7202 | 103900 | 0.0017 | - |
| 0.7209 | 104000 | 0.0017 | - |
| 0.7216 | 104100 | 0.0018 | - |
| 0.7223 | 104200 | 0.0017 | - |
| 0.7230 | 104300 | 0.0017 | - |
| 0.7236 | 104400 | 0.0016 | - |
| 0.7243 | 104500 | 0.0017 | - |
| 0.7250 | 104600 | 0.0017 | - |
| 0.7257 | 104700 | 0.0016 | - |
| 0.7264 | 104800 | 0.0017 | - |
| 0.7271 | 104900 | 0.0017 | - |
| 0.7278 | 105000 | 0.0017 | - |
| 0.7285 | 105100 | 0.0016 | - |
| 0.7292 | 105200 | 0.0018 | - |
| 0.7299 | 105300 | 0.0017 | - |
| 0.7306 | 105400 | 0.0017 | - |
| 0.7313 | 105500 | 0.0017 | - |
| 0.7320 | 105600 | 0.0017 | - |
| 0.7327 | 105700 | 0.0017 | - |
| 0.7333 | 105800 | 0.0016 | - |
| 0.7340 | 105900 | 0.0017 | - |
| 0.7347 | 106000 | 0.0016 | - |
| 0.7354 | 106100 | 0.0016 | - |
| 0.7361 | 106200 | 0.0017 | - |
| 0.7368 | 106300 | 0.0017 | - |
| 0.7375 | 106400 | 0.0017 | - |
| 0.7382 | 106500 | 0.0017 | - |
| 0.7389 | 106600 | 0.0016 | - |
| 0.7396 | 106700 | 0.0016 | - |
| 0.7403 | 106800 | 0.0017 | - |
| 0.7410 | 106900 | 0.0016 | - |
| 0.7417 | 107000 | 0.0017 | - |
| 0.7424 | 107100 | 0.0017 | - |
| 0.7431 | 107200 | 0.0016 | - |
| 0.7437 | 107300 | 0.0016 | - |
| 0.7444 | 107400 | 0.0016 | - |
| 0.7451 | 107500 | 0.0016 | - |
| 0.7458 | 107600 | 0.0017 | - |
| 0.7465 | 107700 | 0.0016 | - |
| 0.7472 | 107800 | 0.0017 | - |
| 0.7479 | 107900 | 0.0015 | - |
| 0.7486 | 108000 | 0.0017 | - |
| 0.7493 | 108100 | 0.0016 | - |
| 0.7500 | 108200 | 0.0017 | - |
| 0.7507 | 108300 | 0.0017 | - |
| 0.7514 | 108400 | 0.0017 | - |
| 0.7521 | 108500 | 0.0016 | - |
| 0.7528 | 108600 | 0.0017 | - |
| 0.7534 | 108700 | 0.0017 | - |
| 0.7541 | 108800 | 0.0015 | - |
| 0.7548 | 108900 | 0.0016 | - |
| 0.7555 | 109000 | 0.0016 | - |
| 0.7562 | 109100 | 0.0016 | - |
| 0.7569 | 109200 | 0.0016 | - |
| 0.7576 | 109300 | 0.0016 | - |
| 0.7583 | 109400 | 0.0017 | - |
| 0.7590 | 109500 | 0.0016 | - |
| 0.7597 | 109600 | 0.0016 | - |
| 0.7604 | 109700 | 0.0016 | - |
| 0.7611 | 109800 | 0.0017 | - |
| 0.7618 | 109900 | 0.0017 | - |
| 0.7625 | 110000 | 0.0016 | - |
| 0.7632 | 110100 | 0.0018 | - |
| 0.7638 | 110200 | 0.0017 | - |
| 0.7645 | 110300 | 0.0017 | - |
| 0.7652 | 110400 | 0.0016 | - |
| 0.7659 | 110500 | 0.0016 | - |
| 0.7666 | 110600 | 0.0016 | - |
| 0.7673 | 110700 | 0.0016 | - |
| 0.7680 | 110800 | 0.0017 | - |
| 0.7687 | 110900 | 0.0017 | - |
| 0.7694 | 111000 | 0.0017 | - |
| 0.7701 | 111100 | 0.0018 | - |
| 0.7708 | 111200 | 0.0016 | - |
| 0.7715 | 111300 | 0.0016 | - |
| 0.7722 | 111400 | 0.0016 | - |
| 0.7729 | 111500 | 0.0016 | - |
| 0.7735 | 111600 | 0.0017 | - |
| 0.7742 | 111700 | 0.0017 | - |
| 0.7749 | 111800 | 0.0016 | - |
| 0.7756 | 111900 | 0.0017 | - |
| 0.7763 | 112000 | 0.0016 | - |
| 0.7770 | 112100 | 0.0016 | - |
| 0.7777 | 112200 | 0.0018 | - |
| 0.7784 | 112300 | 0.0017 | - |
| 0.7791 | 112400 | 0.0017 | - |
| 0.7798 | 112500 | 0.0017 | - |
| 0.7805 | 112600 | 0.0016 | - |
| 0.7812 | 112700 | 0.0016 | - |
| 0.7819 | 112800 | 0.0015 | - |
| 0.7826 | 112900 | 0.0017 | - |
| 0.7833 | 113000 | 0.0016 | - |
| 0.7839 | 113100 | 0.0015 | - |
| 0.7846 | 113200 | 0.0016 | - |
| 0.7853 | 113300 | 0.0016 | - |
| 0.7860 | 113400 | 0.0015 | - |
| 0.7867 | 113500 | 0.0016 | - |
| 0.7874 | 113600 | 0.0016 | - |
| 0.7881 | 113700 | 0.0016 | - |
| 0.7888 | 113800 | 0.0016 | - |
| 0.7895 | 113900 | 0.0016 | - |
| 0.7902 | 114000 | 0.0017 | - |
| 0.7909 | 114100 | 0.0017 | - |
| 0.7916 | 114200 | 0.0016 | - |
| 0.7923 | 114300 | 0.0016 | - |
| 0.7930 | 114400 | 0.0016 | - |
| 0.7937 | 114500 | 0.0016 | - |
| 0.7943 | 114600 | 0.0016 | - |
| 0.7950 | 114700 | 0.0016 | - |
| 0.7957 | 114800 | 0.0016 | - |
| 0.7964 | 114900 | 0.0016 | - |
| 0.7971 | 115000 | 0.0017 | - |
| 0.7978 | 115100 | 0.0016 | - |
| 0.7985 | 115200 | 0.0016 | - |
| 0.7992 | 115300 | 0.0016 | - |
| 0.7999 | 115400 | 0.0015 | - |
| 0.8 | 115416 | - | 0.0011 |
| 0.8006 | 115500 | 0.0016 | - |
| 0.8013 | 115600 | 0.0016 | - |
| 0.8020 | 115700 | 0.0017 | - |
| 0.8027 | 115800 | 0.0016 | - |
| 0.8034 | 115900 | 0.0017 | - |
| 0.8040 | 116000 | 0.0016 | - |
| 0.8047 | 116100 | 0.0016 | - |
| 0.8054 | 116200 | 0.0017 | - |
| 0.8061 | 116300 | 0.0016 | - |
| 0.8068 | 116400 | 0.0016 | - |
| 0.8075 | 116500 | 0.0017 | - |
| 0.8082 | 116600 | 0.0016 | - |
| 0.8089 | 116700 | 0.0016 | - |
| 0.8096 | 116800 | 0.0016 | - |
| 0.8103 | 116900 | 0.0016 | - |
| 0.8110 | 117000 | 0.0015 | - |
| 0.8117 | 117100 | 0.0016 | - |
| 0.8124 | 117200 | 0.0016 | - |
| 0.8131 | 117300 | 0.0016 | - |
| 0.8138 | 117400 | 0.0017 | - |
| 0.8144 | 117500 | 0.0016 | - |
| 0.8151 | 117600 | 0.0017 | - |
| 0.8158 | 117700 | 0.0016 | - |
| 0.8165 | 117800 | 0.0016 | - |
| 0.8172 | 117900 | 0.0017 | - |
| 0.8179 | 118000 | 0.0016 | - |
| 0.8186 | 118100 | 0.0016 | - |
| 0.8193 | 118200 | 0.0016 | - |
| 0.8200 | 118300 | 0.0016 | - |
| 0.8207 | 118400 | 0.0017 | - |
| 0.8214 | 118500 | 0.0016 | - |
| 0.8221 | 118600 | 0.0016 | - |
| 0.8228 | 118700 | 0.0017 | - |
| 0.8235 | 118800 | 0.0016 | - |
| 0.8241 | 118900 | 0.0016 | - |
| 0.8248 | 119000 | 0.0016 | - |
| 0.8255 | 119100 | 0.0016 | - |
| 0.8262 | 119200 | 0.0015 | - |
| 0.8269 | 119300 | 0.0016 | - |
| 0.8276 | 119400 | 0.0016 | - |
| 0.8283 | 119500 | 0.0015 | - |
| 0.8290 | 119600 | 0.0016 | - |
| 0.8297 | 119700 | 0.0015 | - |
| 0.8304 | 119800 | 0.0015 | - |
| 0.8311 | 119900 | 0.0015 | - |
| 0.8318 | 120000 | 0.0015 | - |
| 0.8325 | 120100 | 0.0016 | - |
| 0.8332 | 120200 | 0.0017 | - |
| 0.8339 | 120300 | 0.0016 | - |
| 0.8345 | 120400 | 0.0016 | - |
| 0.8352 | 120500 | 0.0017 | - |
| 0.8359 | 120600 | 0.0016 | - |
| 0.8366 | 120700 | 0.0016 | - |
| 0.8373 | 120800 | 0.0015 | - |
| 0.8380 | 120900 | 0.0016 | - |
| 0.8387 | 121000 | 0.0016 | - |
| 0.8394 | 121100 | 0.0016 | - |
| 0.8401 | 121200 | 0.0016 | - |
| 0.8408 | 121300 | 0.0015 | - |
| 0.8415 | 121400 | 0.0016 | - |
| 0.8422 | 121500 | 0.0016 | - |
| 0.8429 | 121600 | 0.0016 | - |
| 0.8436 | 121700 | 0.0016 | - |
| 0.8443 | 121800 | 0.0017 | - |
| 0.8449 | 121900 | 0.0016 | - |
| 0.8456 | 122000 | 0.0017 | - |
| 0.8463 | 122100 | 0.0016 | - |
| 0.8470 | 122200 | 0.0015 | - |
| 0.8477 | 122300 | 0.0016 | - |
| 0.8484 | 122400 | 0.0016 | - |
| 0.8491 | 122500 | 0.0015 | - |
| 0.8498 | 122600 | 0.0015 | - |
| 0.8505 | 122700 | 0.0016 | - |
| 0.8512 | 122800 | 0.0015 | - |
| 0.8519 | 122900 | 0.0017 | - |
| 0.8526 | 123000 | 0.0015 | - |
| 0.8533 | 123100 | 0.0017 | - |
| 0.8540 | 123200 | 0.0015 | - |
| 0.8546 | 123300 | 0.0015 | - |
| 0.8553 | 123400 | 0.0015 | - |
| 0.8560 | 123500 | 0.0015 | - |
| 0.8567 | 123600 | 0.0016 | - |
| 0.8574 | 123700 | 0.0015 | - |
| 0.8581 | 123800 | 0.0016 | - |
| 0.8588 | 123900 | 0.0016 | - |
| 0.8595 | 124000 | 0.0015 | - |
| 0.8602 | 124100 | 0.0016 | - |
| 0.8609 | 124200 | 0.0015 | - |
| 0.8616 | 124300 | 0.0016 | - |
| 0.8623 | 124400 | 0.0015 | - |
| 0.8630 | 124500 | 0.0016 | - |
| 0.8637 | 124600 | 0.0016 | - |
| 0.8644 | 124700 | 0.0017 | - |
| 0.8650 | 124800 | 0.0016 | - |
| 0.8657 | 124900 | 0.0016 | - |
| 0.8664 | 125000 | 0.0016 | - |
| 0.8671 | 125100 | 0.0016 | - |
| 0.8678 | 125200 | 0.0016 | - |
| 0.8685 | 125300 | 0.0016 | - |
| 0.8692 | 125400 | 0.0015 | - |
| 0.8699 | 125500 | 0.0015 | - |
| 0.8706 | 125600 | 0.0016 | - |
| 0.8713 | 125700 | 0.0016 | - |
| 0.8720 | 125800 | 0.0015 | - |
| 0.8727 | 125900 | 0.0016 | - |
| 0.8734 | 126000 | 0.0015 | - |
| 0.8741 | 126100 | 0.0016 | - |
| 0.8747 | 126200 | 0.0016 | - |
| 0.8754 | 126300 | 0.0015 | - |
| 0.8761 | 126400 | 0.0016 | - |
| 0.8768 | 126500 | 0.0015 | - |
| 0.8775 | 126600 | 0.0015 | - |
| 0.8782 | 126700 | 0.0016 | - |
| 0.8789 | 126800 | 0.0016 | - |
| 0.8796 | 126900 | 0.0015 | - |
| 0.8803 | 127000 | 0.0015 | - |
| 0.8810 | 127100 | 0.0015 | - |
| 0.8817 | 127200 | 0.0016 | - |
| 0.8824 | 127300 | 0.0015 | - |
| 0.8831 | 127400 | 0.0015 | - |
| 0.8838 | 127500 | 0.0015 | - |
| 0.8845 | 127600 | 0.0016 | - |
| 0.8851 | 127700 | 0.0015 | - |
| 0.8858 | 127800 | 0.0016 | - |
| 0.8865 | 127900 | 0.0016 | - |
| 0.8872 | 128000 | 0.0016 | - |
| 0.8879 | 128100 | 0.0016 | - |
| 0.8886 | 128200 | 0.0015 | - |
| 0.8893 | 128300 | 0.0015 | - |
| 0.8900 | 128400 | 0.0015 | - |
| 0.8907 | 128500 | 0.0015 | - |
| 0.8914 | 128600 | 0.0015 | - |
| 0.8921 | 128700 | 0.0017 | - |
| 0.8928 | 128800 | 0.0016 | - |
| 0.8935 | 128900 | 0.0016 | - |
| 0.8942 | 129000 | 0.0016 | - |
| 0.8948 | 129100 | 0.0015 | - |
| 0.8955 | 129200 | 0.0016 | - |
| 0.8962 | 129300 | 0.0016 | - |
| 0.8969 | 129400 | 0.0015 | - |
| 0.8976 | 129500 | 0.0016 | - |
| 0.8983 | 129600 | 0.0014 | - |
| 0.8990 | 129700 | 0.0016 | - |
| 0.8997 | 129800 | 0.0015 | - |
| 0.9004 | 129900 | 0.0016 | - |
| 0.9011 | 130000 | 0.0016 | - |
| 0.9018 | 130100 | 0.0015 | - |
| 0.9025 | 130200 | 0.0015 | - |
| 0.9032 | 130300 | 0.0015 | - |
| 0.9039 | 130400 | 0.0016 | - |
| 0.9046 | 130500 | 0.0015 | - |
| 0.9052 | 130600 | 0.0016 | - |
| 0.9059 | 130700 | 0.0016 | - |
| 0.9066 | 130800 | 0.0016 | - |
| 0.9073 | 130900 | 0.0015 | - |
| 0.9080 | 131000 | 0.0015 | - |
| 0.9087 | 131100 | 0.0015 | - |
| 0.9094 | 131200 | 0.0015 | - |
| 0.9101 | 131300 | 0.0015 | - |
| 0.9108 | 131400 | 0.0015 | - |
| 0.9115 | 131500 | 0.0016 | - |
| 0.9122 | 131600 | 0.0015 | - |
| 0.9129 | 131700 | 0.0016 | - |
| 0.9136 | 131800 | 0.0015 | - |
| 0.9143 | 131900 | 0.0016 | - |
| 0.9150 | 132000 | 0.0015 | - |
| 0.9156 | 132100 | 0.0015 | - |
| 0.9163 | 132200 | 0.0016 | - |
| 0.9170 | 132300 | 0.0016 | - |
| 0.9177 | 132400 | 0.0015 | - |
| 0.9184 | 132500 | 0.0015 | - |
| 0.9191 | 132600 | 0.0016 | - |
| 0.9198 | 132700 | 0.0015 | - |
| 0.9205 | 132800 | 0.0015 | - |
| 0.9212 | 132900 | 0.0015 | - |
| 0.9219 | 133000 | 0.0015 | - |
| 0.9226 | 133100 | 0.0015 | - |
| 0.9233 | 133200 | 0.0015 | - |
| 0.9240 | 133300 | 0.0015 | - |
| 0.9247 | 133400 | 0.0015 | - |
| 0.9253 | 133500 | 0.0015 | - |
| 0.9260 | 133600 | 0.0017 | - |
| 0.9267 | 133700 | 0.0015 | - |
| 0.9274 | 133800 | 0.0015 | - |
| 0.9281 | 133900 | 0.0015 | - |
| 0.9288 | 134000 | 0.0015 | - |
| 0.9295 | 134100 | 0.0015 | - |
| 0.9302 | 134200 | 0.0015 | - |
| 0.9309 | 134300 | 0.0014 | - |
| 0.9316 | 134400 | 0.0014 | - |
| 0.9323 | 134500 | 0.0015 | - |
| 0.9330 | 134600 | 0.0016 | - |
| 0.9337 | 134700 | 0.0015 | - |
| 0.9344 | 134800 | 0.0014 | - |
| 0.9351 | 134900 | 0.0015 | - |
| 0.9357 | 135000 | 0.0016 | - |
| 0.9364 | 135100 | 0.0015 | - |
| 0.9371 | 135200 | 0.0015 | - |
| 0.9378 | 135300 | 0.0016 | - |
| 0.9385 | 135400 | 0.0016 | - |
| 0.9392 | 135500 | 0.0015 | - |
| 0.9399 | 135600 | 0.0015 | - |
| 0.9406 | 135700 | 0.0015 | - |
| 0.9413 | 135800 | 0.0016 | - |
| 0.9420 | 135900 | 0.0015 | - |
| 0.9427 | 136000 | 0.0015 | - |
| 0.9434 | 136100 | 0.0015 | - |
| 0.9441 | 136200 | 0.0016 | - |
| 0.9448 | 136300 | 0.0015 | - |
| 0.9454 | 136400 | 0.0015 | - |
| 0.9461 | 136500 | 0.0015 | - |
| 0.9468 | 136600 | 0.0015 | - |
| 0.9475 | 136700 | 0.0015 | - |
| 0.9482 | 136800 | 0.0015 | - |
| 0.9489 | 136900 | 0.0015 | - |
| 0.9496 | 137000 | 0.0015 | - |
| 0.9503 | 137100 | 0.0016 | - |
| 0.9510 | 137200 | 0.0015 | - |
| 0.9517 | 137300 | 0.0015 | - |
| 0.9524 | 137400 | 0.0015 | - |
| 0.9531 | 137500 | 0.0016 | - |
| 0.9538 | 137600 | 0.0015 | - |
| 0.9545 | 137700 | 0.0015 | - |
| 0.9552 | 137800 | 0.0016 | - |
| 0.9558 | 137900 | 0.0016 | - |
| 0.9565 | 138000 | 0.0016 | - |
| 0.9572 | 138100 | 0.0015 | - |
| 0.9579 | 138200 | 0.0015 | - |
| 0.9586 | 138300 | 0.0015 | - |
| 0.9593 | 138400 | 0.0015 | - |
| 0.9600 | 138500 | 0.0015 | - |
| 0.9607 | 138600 | 0.0015 | - |
| 0.9614 | 138700 | 0.0015 | - |
| 0.9621 | 138800 | 0.0015 | - |
| 0.9628 | 138900 | 0.0014 | - |
| 0.9635 | 139000 | 0.0016 | - |
| 0.9642 | 139100 | 0.0015 | - |
| 0.9649 | 139200 | 0.0014 | - |
| 0.9656 | 139300 | 0.0015 | - |
| 0.9662 | 139400 | 0.0015 | - |
| 0.9669 | 139500 | 0.0015 | - |
| 0.9676 | 139600 | 0.0015 | - |
| 0.9683 | 139700 | 0.0015 | - |
| 0.9690 | 139800 | 0.0015 | - |
| 0.9697 | 139900 | 0.0015 | - |
| 0.9704 | 140000 | 0.0014 | - |
| 0.9711 | 140100 | 0.0014 | - |
| 0.9718 | 140200 | 0.0015 | - |
| 0.9725 | 140300 | 0.0015 | - |
| 0.9732 | 140400 | 0.0015 | - |
| 0.9739 | 140500 | 0.0015 | - |
| 0.9746 | 140600 | 0.0016 | - |
| 0.9753 | 140700 | 0.0015 | - |
| 0.9759 | 140800 | 0.0015 | - |
| 0.9766 | 140900 | 0.0015 | - |
| 0.9773 | 141000 | 0.0016 | - |
| 0.9780 | 141100 | 0.0015 | - |
| 0.9787 | 141200 | 0.0015 | - |
| 0.9794 | 141300 | 0.0016 | - |
| 0.9801 | 141400 | 0.0015 | - |
| 0.9808 | 141500 | 0.0014 | - |
| 0.9815 | 141600 | 0.0014 | - |
| 0.9822 | 141700 | 0.0015 | - |
| 0.9829 | 141800 | 0.0016 | - |
| 0.9836 | 141900 | 0.0015 | - |
| 0.9843 | 142000 | 0.0015 | - |
| 0.9850 | 142100 | 0.0015 | - |
| 0.9857 | 142200 | 0.0015 | - |
| 0.9863 | 142300 | 0.0014 | - |
| 0.9870 | 142400 | 0.0016 | - |
| 0.9877 | 142500 | 0.0016 | - |
| 0.9884 | 142600 | 0.0015 | - |
| 0.9891 | 142700 | 0.0015 | - |
| 0.9898 | 142800 | 0.0014 | - |
| 0.9905 | 142900 | 0.0015 | - |
| 0.9912 | 143000 | 0.0015 | - |
| 0.9919 | 143100 | 0.0014 | - |
| 0.9926 | 143200 | 0.0014 | - |
| 0.9933 | 143300 | 0.0015 | - |
| 0.9940 | 143400 | 0.0015 | - |
| 0.9947 | 143500 | 0.0015 | - |
| 0.9954 | 143600 | 0.0015 | - |
| 0.9960 | 143700 | 0.0015 | - |
| 0.9967 | 143800 | 0.0015 | - |
| 0.9974 | 143900 | 0.0015 | - |
| 0.9981 | 144000 | 0.0015 | - |
| 0.9988 | 144100 | 0.0015 | - |
| 0.9995 | 144200 | 0.0015 | - |
| 1.0 | 144270 | - | 0.0010 |