diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,2091 @@ +--- +language: +- en +license: apache-2.0 +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:80543469 +- loss:MatryoshkaLoss +- loss:MultipleNegativesRankingLoss +widget: +- source_sentence: What was the Office of Foods in charge of? + sentences: + - This area, stretching northward from the centrally located Great Hall of State, + is believed to have been the site of the Office of Foods. This office stocked + foods other than the rice that was paid as tax, and was in charge of providing + meals for state banquets and rituals held in the palace. + - In 2002, Barclay Records, then as part of Universal Music France, released a digitally + remastered version of the original vinyl in CD and in 10" (25 cm) vinyl record + (LP), under the same name, as part of a compilation containing re-releases of + all of Dalida's studio albums recorded under the Barclay label. The album was + again re-released in 2005. + - Kevin Jon Davies is a British television and video director primarily associated + with documentaries and spin-off videos associated with "Doctor Who", "The Hitchhiker's + Guide to the Galaxy" and "Blake's 7". He also worked on the BAFTA award-winning + animation sequences of the 1981 "Hitchhiker's Guide" television adaptation. +- source_sentence: jak wysłać sf do krs? + sentences: + - '[''Kliknij na pole „Przygotowanie i składanie zgłoszeń”'', ''Następnie kliknij + niebieski przycisk Dodaj zgłoszenie i uzupełnij o numer KRS Twojej jednostki na + stronie „Rejestracja nowego zgłoszenia – Krok 1”'']' + - When you experience a trigger, the insides of your airways swell even more. This + narrows the space for air to move in and out of the lungs. The muscles that wrap + around your airways also can tighten, making breathing even harder. When that + happens, it's called an asthma flare-up, asthma episode or asthma "attack." + - Lie (L) – The Lie scale is intended to identify individuals who are deliberately + trying to avoid answering the MMPI honestly and in a frank manner. The scale measures + attitudes and practices that are culturally laudable, but rarely found in most + people. +- source_sentence: 'Air Pollution in Eastern Asia: An Integrated Perspective: Chapter + 14: Observation of Air Pollution over China Using the IASI Thermal Infrared Space + Sensor' + sentences: + - In this chapter we describe what is achievable in terms of pollutant tracking + from space using observations provided by thermal infrared remote sensors. After + a general introduction on infrared remote sensing, we exploit the data provided + by the Infrared Atmospheric Sounding Interferometer (IASI) missions onboard the + Metop series of satellite to illustrate pollution detection at various spatial + and temporal scales. Then, we focus on air pollution over China and discuss three + case studies involving different pollutants. The first example discusses the geophysical + conditions for detection of ammonia (NH3) and sulfur dioxide (SO2), both precursors + of particulate matter (PM). The second case illustrates the seasonal variation + of ozone (O3), in particular during the monsoon period. The third case shows the + local accumulation of enhanced levels of carbon monoxide (CO) when pollution episodes + occur. + - This article explores the political aspects of Islamic parties in Jombang, East + Java. The issue came about during the controversy arising out of the defection + of the leader of the Tarekat Qadiriyah Wa Naqsyabandiyah, an Islamic order, prior + to the 1977 general election. It raised questions as to the political orientation + of Islamic groups in Indonesia during the 1970s and 1980s. + - Abstract Unexpected findings on bone scintigraphy such as asymmetrical uptake + in extremities may cause confusion for the diagnosis. The authors describe three + cases of accidental intraarterial injection of Tc- 99m methylene diphosphonate + ( 99m Tc-MDP) on the antecubital region and discuss the findings and differential + diagnosis. +- source_sentence: What type of stimuli do nociceptors response to? + sentences: + - 'After independence, Dutch was dropped as an official language and replaced by + Malay. Yet the Indonesian language inherited many words from Dutch: words for + everyday life as well as scientific and technological terms. One scholar argues + that 20% of Indonesian words can be traced back to Dutch words, many of which + are transliterated to reflect phonetic pronunciation e.g. kantoor (Dutch for "office") + in Indonesian is kantor, while bus ("bus") becomes bis. In addition, many Indonesian + words are calques on Dutch, for example, rumah sakit (Indonesian for "hospital") + is calqued on the Dutch ziekenhuis (literally "house of the sick"), kebun binatang + ("zoo") on dierentuin (literally "animal garden"), undang-undang dasar ("constitution") + from grondwet (literally "ground law"). These account for some of the differences + in vocabulary between Indonesian and Malay.' + - Wilhelm Erb's (1874) "intensive" theory, that a pain signal can be generated by + intense enough stimulation of any sensory receptor, has been soundly disproved. + Some sensory fibers do not differentiate between noxious and non-noxious stimuli, + while others, nociceptors, respond only to noxious, high intensity stimuli. At + the peripheral end of the nociceptor, noxious stimuli generate currents that, + above a given threshold, begin to send signals along the nerve fiber to the spinal + cord. The "specificity" (whether it responds to thermal, chemical or mechanical + features of its environment) of a nociceptor is determined by which ion channels + it expresses at its peripheral end. Dozens of different types of nociceptor ion + channels have so far been identified, and their exact functions are still being + determined. + - The first attempt to establish a proper governing body and adopted the current + set of Rugby rules was the Foot Ball Association of Canada, organized on March + 24, 1873 followed by the Canadian Rugby Football Union (CRFU) founded June 12, + 1880, which included teams from Ontario and Quebec. Later both the Ontario and + Quebec Rugby Football Union (ORFU and QRFU) were formed (January 1883), and then + the Interprovincial (1907) and Western Interprovincial Football Union (1936) (IRFU + and WIFU). The CRFU reorganized into an umbrella organization forming the Canadian + Rugby Union (CRU) in 1891. The original forerunners to the current Canadian Football + League, was established in 1956 when the IRFU and WIFU formed an umbrella organization, + The Canadian Football Council (CFC). And then in 1958 the CFC left The CRFU to + become The CFL. +- source_sentence: 'Gadofosveset-enhanced MR angiography of carotid arteries: does + steady-state imaging improve accuracy of first-pass imaging?' + sentences: + - Prior studies have demonstrated improved clinical outcomes for surgeons with a + high-volume experience with certain open vascular operations. A high-volume experience + with carotid artery stenting (CAS) improves clinical outcomes. Moreover, it is + not known whether experience with other endovascular procedures, including percutaneous + coronary interventions (PCIs), is an adequate substitute for experience with CAS. + The goal of this study was to quantify the effect of increasing clinician volume + of CAS, endovascular aneurysm repair (EVAR), and thoracic endovascular aortic + aneurysm repair (TEVAR), and PCI on the outcomes for CAS. + - While sensitive to internal carotid artery (ICA) occlusion, carotid ultrasound + can produce false-positive results. CT angiography (CTA) has a high specificity + for ICA occlusion and is safer and cheaper than catheter angiography, although + less accurate. We determined the cost-effectiveness of CTA versus catheter angiography + for confirming an ICA occlusion first suggested by carotid ultrasound. + - Brachial artery FMD and GMD and carotid intima media thickness (cIMT) were studied + using ultrasound in 20 patients diagnosed with early RA in whom symptoms had been + present for less than 12 months, and in 20 control subjects matched for age, sex + and established cardiovascular risk factors. FMD and GMD were re-assessed after + 12 months in RA patients and the change in each parameter was calculated. Data + were analysed by univariate regression. + - Compared with preoperative clinical and conventional MR data, (1)H MRS improved + the accuracy of MR imaging from 60.9% to 83%. We found (1)H MRS reliably distinguished + between abscess and high-grade tumour, and between high-grade glioma and low-grade + glioma, but was not able to reliably distinguish between recurrent glioma and + radiation necrosis. In 12/23 cases (52%) the (1)H MRS findings positively altered + our clinical management. Two representative cases are presented. + - Different in-plane resolutions have been used for carotid 3T MRI. We compared + the reproducibility, as well as the within- and between reader variability of + high and routinely used spatial resolution in scans of patients with atherosclerotic + carotid artery disease. Since no consensus exists about the optimal segmentation + method, we analysed all imaging data using two different segmentation methods. + - In the population-based Prospective Investigation of the Vasculature in Uppsala + Seniors (PIVUS) study (1016 subjects all aged 70), the prevalence of overt plaques + and echogenectity (grey scale median, GSM) of carotid artery plaques were recorded + by ultrasound in both of the carotid arteries. The thickness (IMT) and echogenicity + (IM-GSM) of the intima-media complex were also measured. Bisphenol A (BPA) and + 10 phthalate metabolites were analyzed in serum by a API 4000 liquid chromatograph/tandem + mass spectrometer. + - In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, + applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental + autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice + were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after + adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios + were calculated individually for different CVO and correlated to the clinical + disease course. Subsequently, the tissue distribution of fluorescence-labeled + Gf as well as the extent of cellular inflammation was assessed in corresponding + histological slices. + - Development of an improved MR sequence for examining the lung. + - Thirty-seven carotid artery disease patients participated in this study, of whom + 24 underwent magnetic resonance imaging before and after CEA. Seventeen control + subjects spanning 5 decades underwent magnetic resonance imaging to assess age-related + changes. Hemodynamic metrics (that is, relative time to peak and amplitude) were + calculated with a γ-variate model. Linear regression was used to relate carotid + artery disease burden to downstream hemodynamics in the circle of Willis. + - Carotid artery stenting (CAS) is associated with a higher risk of both hemodynamic + depression and new ischemic brain lesions on diffusion-weighted imaging than carotid + endarterectomy (CEA). We assessed whether the occurrence of hemodynamic depression + is associated with these lesions in patients with symptomatic carotid stenosis + treated by CAS or CEA in the randomized International Carotid Stenting Study (ICSS)-MRI + substudy. + - Magnetic resonance imaging (MRI) guidance may improve the accuracy of Gleason + score (GS) determination by directing the biopsy to regions of interest (ROI) + that are likely to harbor high-grade prostate cancer (CaP). The aim of this study + was to determine the frequency and predictors of GS upgrading when a subsequent + MRI-guided biopsy is performed on patients with a diagnosis of GS 6 disease on + the basis of conventional, transrectal ultrasound-guided biopsy. + - Measures of carotid-femoral pulse wave velocity (cf-PWV) and carotid augmentation + index (cAI) may be affected by the presence of an abdominal aortic aneurysm (AAA). + We, therefore, investigated series of various measures of arterial stiffness and + wave reflections in patients with AAA, before and 4 weeks after endovascular aneurysm + repair (EVAR). + - High spatial resolution of dynamic contrast-enhanced (DCE) MR imaging allows characterization + of heterogenous tumor microenvironment. Our purpose was to determine which is + the best advanced MR imaging protocol, focused on additional MR perfusion method, + for predicting recurrent metastatic brain tumor following gamma-knife radiosurgery + (GKRS). + - To determine whether acromegalic patients have increased thyroidal vascularity + and blood flow on colour flow Doppler sonography (CFDS). + - To investigate whether an existing method for correction of phase offset errors + in phase-contrast velocity quantification is applicable for assessment of main + pulmonary artery flow with an MR scanner equipped with a high-power gradient system. + - Consecutive patients (n = 292) undergoing carotid endarterectomy for symptomatic + and asymptomatic carotid stenosis were included in the study. Mortality and cardiovascular + ischemic events were recorded during a median follow-up of 5.2 years. Baseline + plasma concentrations of adiponectin were measured. Cox regression models stratified + for gender were used for estimation of risk of events. + - To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance + (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction + angiography (DSA) as the reference standard, and to determine the value of reading + first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms. + - Phase-contrast Cardiovascular Magnetic Resonance Imaging (CMR) generally requires + the analysis of stationary tissue adjacent to a blood vessel to serve as a baseline + reference for zero velocity. However, for the heart and great vessels, there is + often no stationary tissue immediately adjacent to the vessel. Consequently, uncorrected + velocity offsets may introduce substantial errors in flow quantification. The + purpose of this study was to assess the magnitude of these flow errors and to + validate a clinically applicable method for their correction. + - This study was a post hoc analysis of a prospective cohort comprising 485 consecutive + patients undergoing carotid endarterectomy for high-grade ICAS. Patients were + classified by their clinical presentation, ie, asymptomatic (n = 213) or symptomatic + (within 6 months of surgery; n = 272, comprising both transient ischemic attack + [TIA; n = 163] and stroke [n = 109]). We investigated the association of cl-ICAS + with the primary outcome in adjusted regression models. + - Several studies reported on the moderate diagnostic yield of elective invasive + coronary angiography (ICA) regarding the presence of coronary artery disease (CAD), + but limited data are available on how prior testing for ischaemia may contribute + to improve the diagnostic yield in an every-day clinical setting. This study aimed + to assess the value and use of cardiac myocardial perfusion single photon emission + computed tomography (MPS) in patient selection prior to elective ICA. + - The feasibility of carotid stenting (CS) is no longer questionable, although its + indications remain debatable. Until the results of randomized trials are available, + personal series and registries should help in the comparison of long-term results + of CS with those of endarterectomy. We report here the long-term results of a + large series of CS in our department with a long follow-up. This retrospective + study reviews a single surgeon's 11-year experience with CS. Our results are compared + with those of conventional surgery emanating from our own series and the North + American Symptomatic Carotid Endarterectomy Trial (NASCET), European Carotid Surgery + Trial (ECST), and Asymptomatic Carotid Atherosclerosis Study (ACAS). +datasets: +- sentence-transformers/gooaq +- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 +- sentence-transformers/squad +- sentence-transformers/s2orc +- sentence-transformers/all-nli +- sentence-transformers/paq +- sentence-transformers/trivia-qa +pipeline_tag: sentence-similarity +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 +co2_eq_emissions: + emissions: 1014.8766030829654 + energy_consumed: 2.610937435575236 + source: codecarbon + training_type: fine-tuning + on_cloud: false + cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K + ram_total_size: 31.777088165283203 + hours_used: 17.883 + hardware_used: 1 x NVIDIA GeForce RTX 3090 +model-index: +- name: Static Embeddings with BERT uncased tokenizer finetuned on various datasets + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoClimateFEVER + type: NanoClimateFEVER + metrics: + - type: cosine_accuracy@1 + value: 0.32 + 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: 0.78 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.32 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.19333333333333333 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.14 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.10399999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.14666666666666664 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.239 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.27899999999999997 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.4196666666666667 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.33085031011968163 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.44530158730158725 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.259819611075427 + 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: 0.84 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.94 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.5866666666666666 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.544 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.45199999999999996 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.0804732343549837 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.16047472236902457 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.21798474210348842 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.31433571884014205 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.5681388031303078 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.7853888888888889 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.4334843491187922 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoFEVER + type: NanoFEVER + metrics: + - type: cosine_accuracy@1 + value: 0.46 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.8 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.84 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.94 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.46 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.26666666666666666 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.18 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.09999999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.4366666666666667 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.7466666666666667 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.8033333333333332 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.9033333333333333 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.6921500788245725 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.639690476190476 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.62054338159709 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoFiQA2018 + type: NanoFiQA2018 + metrics: + - type: cosine_accuracy@1 + value: 0.28 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.44 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.54 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.64 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.28 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.19333333333333333 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.16 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.10399999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.15188888888888888 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.29826984126984124 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.3792936507936508 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.4837936507936508 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.3651145030243953 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.3914603174603174 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.3023673541934707 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoHotpotQA + type: NanoHotpotQA + metrics: + - type: cosine_accuracy@1 + value: 0.64 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.82 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.86 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.96 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.64 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.3733333333333333 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.26 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.14799999999999996 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.32 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.56 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.65 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.74 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.6547177705459605 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.7485238095238096 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.5797919554359183 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoMSMARCO + type: NanoMSMARCO + metrics: + - type: cosine_accuracy@1 + value: 0.18 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.42 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.5 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.66 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.18 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.13999999999999999 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.10000000000000002 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.066 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.18 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.42 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.5 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.66 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.4040678769319761 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.3244682539682539 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.33886403445504565 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoNFCorpus + type: NanoNFCorpus + metrics: + - type: cosine_accuracy@1 + value: 0.42 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.56 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.62 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.72 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.42 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.3733333333333333 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.32 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.244 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.04278202363094378 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.09842444348194118 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.11962677523904507 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.1389182072247147 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.3241949561078219 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.5040793650793652 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.1448579573714899 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoNQ + type: NanoNQ + metrics: + - type: cosine_accuracy@1 + value: 0.24 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.44 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.58 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.7 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.24 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.14666666666666667 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.124 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.07600000000000001 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.24 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.43 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.58 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.69 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.4533881733265689 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.3764047619047619 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.3890107375543526 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoQuoraRetrieval + type: NanoQuoraRetrieval + metrics: + - type: cosine_accuracy@1 + value: 0.8 + 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: 0.8 + 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: 0.12999999999999998 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.7106666666666667 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.9253333333333333 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.9626666666666668 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.9793333333333334 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.895097527564125 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.88 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.8594406482406483 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoSCIDOCS + type: NanoSCIDOCS + metrics: + - type: cosine_accuracy@1 + value: 0.28 + 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: 0.7 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.28 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.22666666666666666 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.188 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.14 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.059666666666666666 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.14166666666666666 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.19466666666666668 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.2886666666666667 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.26425784158945775 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.39979365079365076 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.20502449880105952 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoArguAna + type: NanoArguAna + metrics: + - type: cosine_accuracy@1 + value: 0.1 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.46 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.56 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.74 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.1 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.15333333333333332 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.11200000000000003 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.07400000000000001 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.1 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.46 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.56 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.74 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.4077879341218404 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.3033888888888889 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.31510434322531095 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoSciFact + type: NanoSciFact + metrics: + - type: cosine_accuracy@1 + value: 0.52 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.6 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.62 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.76 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.52 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.20666666666666667 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.132 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.08399999999999999 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.485 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.57 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.595 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.75 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.6111476167014296 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.5836904761904762 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.5683309026222819 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: NanoTouche2020 + type: NanoTouche2020 + metrics: + - type: cosine_accuracy@1 + value: 0.5714285714285714 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.8979591836734694 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9795918367346939 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 1.0 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.5714285714285714 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.6054421768707482 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.6204081632653061 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.5306122448979592 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.03980518443040866 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.12364050983083796 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.20953289383493803 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.33697859476017505 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.5702638593808323 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.744047619047619 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.4469881140237455 + name: Cosine Map@100 + - task: + type: nano-beir + name: Nano BEIR + dataset: + name: NanoBEIR mean + type: NanoBEIR_mean + metrics: + - type: cosine_accuracy@1 + value: 0.4239560439560439 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.6336891679748822 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.7030455259026686 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.8107692307692307 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.4239560439560439 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.2963160648874934 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.24064678178963897 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.17327786499215073 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.2302781536901455 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.3979597064321778 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.4654695945105992 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.5726943208937448 + name: Cosine Recall@10 + - type: cosine_ndcg@10 + value: 0.503167480874536 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.5481721611721612 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.420279068285741 + name: Cosine Map@100 +--- + +# Static Embeddings with BERT uncased tokenizer finetuned on various datasets + +This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [squad](https://huggingface.co/datasets/sentence-transformers/squad), [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc), [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli), [paq](https://huggingface.co/datasets/sentence-transformers/paq) and [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) datasets. 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 + +- **Maximum Sequence Length:** inf tokens +- **Output Dimensionality:** 1024 tokens +- **Similarity Function:** Cosine Similarity +- **Training Datasets:** + - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) + - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) + - [squad](https://huggingface.co/datasets/sentence-transformers/squad) + - [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) + - [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) + - [paq](https://huggingface.co/datasets/sentence-transformers/paq) + - [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) +- **Language:** en +- **License:** apache-2.0 + +### 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): StaticEmbedding( + (embedding): EmbeddingBag(30522, 1024, mode='mean') + ) +) +``` + +## 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("tomaarsen/static-bert-uncased-gooaq-beir-multi-1") +# Run inference +sentences = [ + 'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?', + 'To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms.', + 'In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices.', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 1024] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + +## Evaluation + +### Metrics + +#### Information Retrieval + +* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` +* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) + +| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | +|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| +| cosine_accuracy@1 | 0.32 | 0.7 | 0.46 | 0.28 | 0.64 | 0.18 | 0.42 | 0.24 | 0.8 | 0.28 | 0.1 | 0.52 | 0.5714 | +| cosine_accuracy@3 | 0.52 | 0.84 | 0.8 | 0.44 | 0.82 | 0.42 | 0.56 | 0.44 | 0.96 | 0.48 | 0.46 | 0.6 | 0.898 | +| cosine_accuracy@5 | 0.6 | 0.9 | 0.84 | 0.54 | 0.86 | 0.5 | 0.62 | 0.58 | 1.0 | 0.54 | 0.56 | 0.62 | 0.9796 | +| cosine_accuracy@10 | 0.78 | 0.94 | 0.94 | 0.64 | 0.96 | 0.66 | 0.72 | 0.7 | 1.0 | 0.7 | 0.74 | 0.76 | 1.0 | +| cosine_precision@1 | 0.32 | 0.7 | 0.46 | 0.28 | 0.64 | 0.18 | 0.42 | 0.24 | 0.8 | 0.28 | 0.1 | 0.52 | 0.5714 | +| cosine_precision@3 | 0.1933 | 0.5867 | 0.2667 | 0.1933 | 0.3733 | 0.14 | 0.3733 | 0.1467 | 0.3867 | 0.2267 | 0.1533 | 0.2067 | 0.6054 | +| cosine_precision@5 | 0.14 | 0.544 | 0.18 | 0.16 | 0.26 | 0.1 | 0.32 | 0.124 | 0.248 | 0.188 | 0.112 | 0.132 | 0.6204 | +| cosine_precision@10 | 0.104 | 0.452 | 0.1 | 0.104 | 0.148 | 0.066 | 0.244 | 0.076 | 0.13 | 0.14 | 0.074 | 0.084 | 0.5306 | +| cosine_recall@1 | 0.1467 | 0.0805 | 0.4367 | 0.1519 | 0.32 | 0.18 | 0.0428 | 0.24 | 0.7107 | 0.0597 | 0.1 | 0.485 | 0.0398 | +| cosine_recall@3 | 0.239 | 0.1605 | 0.7467 | 0.2983 | 0.56 | 0.42 | 0.0984 | 0.43 | 0.9253 | 0.1417 | 0.46 | 0.57 | 0.1236 | +| cosine_recall@5 | 0.279 | 0.218 | 0.8033 | 0.3793 | 0.65 | 0.5 | 0.1196 | 0.58 | 0.9627 | 0.1947 | 0.56 | 0.595 | 0.2095 | +| cosine_recall@10 | 0.4197 | 0.3143 | 0.9033 | 0.4838 | 0.74 | 0.66 | 0.1389 | 0.69 | 0.9793 | 0.2887 | 0.74 | 0.75 | 0.337 | +| **cosine_ndcg@10** | **0.3309** | **0.5681** | **0.6922** | **0.3651** | **0.6547** | **0.4041** | **0.3242** | **0.4534** | **0.8951** | **0.2643** | **0.4078** | **0.6111** | **0.5703** | +| cosine_mrr@10 | 0.4453 | 0.7854 | 0.6397 | 0.3915 | 0.7485 | 0.3245 | 0.5041 | 0.3764 | 0.88 | 0.3998 | 0.3034 | 0.5837 | 0.744 | +| cosine_map@100 | 0.2598 | 0.4335 | 0.6205 | 0.3024 | 0.5798 | 0.3389 | 0.1449 | 0.389 | 0.8594 | 0.205 | 0.3151 | 0.5683 | 0.447 | + +#### Nano BEIR + +* Dataset: `NanoBEIR_mean` +* Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) + +| Metric | Value | +|:--------------------|:-----------| +| cosine_accuracy@1 | 0.424 | +| cosine_accuracy@3 | 0.6337 | +| cosine_accuracy@5 | 0.703 | +| cosine_accuracy@10 | 0.8108 | +| cosine_precision@1 | 0.424 | +| cosine_precision@3 | 0.2963 | +| cosine_precision@5 | 0.2406 | +| cosine_precision@10 | 0.1733 | +| cosine_recall@1 | 0.2303 | +| cosine_recall@3 | 0.398 | +| cosine_recall@5 | 0.4655 | +| cosine_recall@10 | 0.5727 | +| **cosine_ndcg@10** | **0.5032** | +| cosine_mrr@10 | 0.5482 | +| cosine_map@100 | 0.4203 | + + + + + +## Training Details + +### Training Datasets + +#### gooaq + +* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) +* Size: 3,012,496 training samples +* Columns: question and answer +* Approximate statistics based on the first 1000 samples: + | | question | answer | + |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | question | answer | + |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | what is the difference between broilers and layers? | An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well. | + | what is the difference between chronological order and spatial order? | As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time. | + | is kamagra same as viagra? | Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### msmarco + +* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) +* Size: 502,939 training samples +* Columns: query, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | query | positive | negative | + |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | query | positive | negative | + |:---------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | when was the sullivan acts | Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law. | Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law. | + | can lavender grow indoors | Growing Lavender Indoors. People ALWAYS ask if you can grow lavender indoors. Well, you can, but most Lavender does best outside. Here is our winter experiment to show you what it would look like. This is one of our 4 Lavender Babies from Fall 2010. Our test specimen is L. x intermedia 'Grosso'. | Lavender can be grown indoors with a bit of effort to keep it in the conditions it loves to thrive. First off begin with choosing a variety that is better able to tolerate the conditions inside a home. To successfully grow Lavender indoors you need to create optimal growing conditions which is hard to do inside a house. | + | what kind of barley do you malt | Barley is a wonderfully versatile cereal grain with a rich nutlike flavor and an appealing chewy, pasta-like consistency. Its appearance resembles wheat berries, although it is slightly lighter in color. Sprouted barley is naturally high in maltose, a sugar that serves as the basis for both malt syrup sweetener. | Specialty grains that can be used in this way are usually barley, malted or unmalted, that has been treated differently at the malting company. Crystal malt is one of the specialty grains. It is available in a whole range of colors, from 20 to 120 Lovibond. Crystal malt is malted barley that is heated while wet. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### squad + +* Dataset: [squad](https://huggingface.co/datasets/sentence-transformers/squad) at [d84c8c2](https://huggingface.co/datasets/sentence-transformers/squad/tree/d84c8c2ef64693264c890bb242d2e73fc0a46c40) +* Size: 87,599 training samples +* Columns: question and answer +* Approximate statistics based on the first 1000 samples: + | | question | answer | + |:--------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | question | answer | + |:-------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | What did Business Insider call San Diego in 2013? | San Diego was ranked as the 20th-safest city in America in 2013 by Business Insider. According to Forbes magazine, San Diego was the ninth-safest city in the top 10 list of safest cities in the U.S. in 2010. Like most major cities, San Diego had a declining crime rate from 1990 to 2000. Crime in San Diego increased in the early 2000s. In 2004, San Diego had the sixth lowest crime rate of any U.S. city with over half a million residents. From 2002 to 2006, the crime rate overall dropped 0.8%, though not evenly by category. While violent crime decreased 12.4% during this period, property crime increased 1.1%. Total property crimes per 100,000 people were lower than the national average in 2008. | + | What did the Spanish call this region? | The name Montana comes from the Spanish word Montaña, meaning "mountain", or more broadly, "mountainous country". Montaña del Norte was the name given by early Spanish explorers to the entire mountainous region of the west. The name Montana was added to a bill by the United States House Committee on Territories, which was chaired at the time by Rep. James Ashley of Ohio, for the territory that would become Idaho Territory. The name was successfully changed by Representatives Henry Wilson (Massachusetts) and Benjamin F. Harding (Oregon), who complained that Montana had "no meaning". When Ashley presented a bill to establish a temporary government in 1864 for a new territory to be carved out of Idaho, he again chose Montana Territory. This time Rep. Samuel Cox, also of Ohio, objected to the name. Cox complained that the name was a misnomer given that most of the territory was not mountainous and that a Native American name would be more appropriate than a Spanish one. Other names such as Shoshone were suggested, but it was eventually decided that the Committee on Territories could name it whatever they wanted, so the original name of Montana was adopted. | + | Small missiles were designed that could be mounted on what? | As this process continued, the missile found itself being used for more and more of the roles formerly filled by guns. First to go were the large weapons, replaced by equally large missile systems of much higher performance. Smaller missiles soon followed, eventually becoming small enough to be mounted on armored cars and tank chassis. These started replacing, or at least supplanting, similar gun-based SPAAG systems in the 1960s, and by the 1990s had replaced almost all such systems in modern armies. Man-portable missiles, MANPADs as they are known today, were introduced in the 1960s and have supplanted or even replaced even the smallest guns in most advanced armies. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### s2orc + +* Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) +* Size: 90,000 training samples +* Columns: title and abstract +* Approximate statistics based on the first 1000 samples: + | | title | abstract | + |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | title | abstract | + |:----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | Modeling Method of Flow Diversion of the Three Outlets in Jingjiang Reach Under Unsteady Flow Conditions | The Yangtze River Flood Protection Physical Model is built under the financial support of World Bank loan.Based on theoretical analysis and experimental study,a modeling method of flow diversion of the three outlets in Jingjiang Reach under unsteady flow conditions was established for the model.Validation tests under both steady and unsteady flow conditions manifested that with this modeling method,the experimental flow diversion proves to be consistent with that of the prototype and therefore meets the requirements for precision.Being validated,this modeling method has been applied to Yangtze River Flood Protection Physical Model to study the flood routing features in Jingjiang reach. | + | Enlightening on medical administration by clinical governance in British | Medical quality and safety were the responsibilities of medical system in view of British clinical governance. Medical regulation institutes were considered to be built and be authorized regulation rights. British medical administration was introduced and its enlightening in China was mentioned. | + | APPLICATION OF A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR SHIPPING COMPANY PERFORMANCE EVALUATION | Combining fuzzy set theory, Analytic Hierarchy Process (AHP) and concept of entropy, a fuzzy Multiple Criteria Decision-Making (MCDM) model for shipping company performance evaluation is proposed. First, the AHP is used to construct subjective weights for all criteria and sub-criteria. Then, linguistic values characterized by triangular fuzzy numbers and trapezoidal fuzzy numbers are used to denote the evaluation values of all alternatives with respect to various subjective and objective criteria. Finally, the aggregation fuzzy assessment of different shipping companies is ranked to determine the best selection. Utilizing this fuzzy MCDM model, the decision-maker's fuzzy assessment and the trade-off between various evaluations criteria can be taken into account in the aggregation process, thus ensuring more effective and accurate decision-making. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### allnli + +* Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) +* Size: 557,850 training samples +* Columns: anchor, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | anchor | positive | negative | + |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | anchor | positive | negative | + |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| + | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. | + | Children smiling and waving at camera | There are children present | The kids are frowning | + | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | The boy skates down the sidewalk. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### paq + +* Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) +* Size: 64,371,441 training samples +* Columns: query and answer +* Approximate statistics based on the first 1000 samples: + | | query | answer | + |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | query | answer | + |:----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | in veetla visheshanga ganesh is the husband of | Veetla Visheshanga a song which reminds Ganga's memory. She is actually not Ganga but Gowri and her lover is the groom named Ganesh. When both were about to marry they were stopped by some goons because of which Gowri fell from the mountain but survived with injuries. Gopal who found the truth brought Ganesh to unite them. Gopal insists Gowri to marry Ganesh as both of them are lovers to which Gowri unwillingly accepts. But while Ganesh tries to tie the Mangal Sutra, Gowri stops him and she goes to Gopal saying that he may not need her but she needs him | + | when did simon property group became a publicly traded company | of the S&P 100. Simon Property Group has been the subject of several lawsuits and investigations regarding civil rights and discrimination. Simon Property Group was formed in 1993 when the majority of the shopping center interests of Melvin Simon & Associates became a publicly traded company. Melvin Simon & Associates, owned by brothers Melvin Simon and Herbert Simon, was founded in 1960 in Indianapolis, Indiana, and had long been one of the top shopping center developers in the United States. In 1996, Simon DeBartolo Group was created when Simon Property merged with former rival DeBartolo Realty Corp. This was shortly | + | what was the nationality of antoine faivre | Theosophy (Boehmian) below. "Theosophy": The scholar of esotericism Wouter Hanegraaff described Christian theosophy as "one of the major currents in the history of Western esotericism". Christian theosophy is an under-researched area; a general history of it has never been written. The French scholar Antoine Faivre had a specific interest in the theosophers and illuminists of the eighteenth and nineteenth centuries. He wrote his doctoral thesis on Karl von Eckartshausen and Christian theosophy. Scholars of esotericism have argued that Faivre's definition of Western esotericism relies on his own specialist focus on Christian theosophy, Renaissance Hermeticism, and Romantic "Naturphilosophie" and therefore creates an "ideal" | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### trivia_qa + +* Dataset: [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) +* Size: 73,346 training samples +* Columns: query and answer +* Approximate statistics based on the first 1000 samples: + | | query | answer | + |:--------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | query | answer | + |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | What type of rock is formed by the solidification of molten magma? | igneous rock - Dictionary Definition : Vocabulary.com igneous rock n rock formed by the solidification of molten magma Types: a rare type of peridotite that sometimes contains diamonds; found in South Africa and Siberia Type of: material consisting of the aggregate of minerals like those making up the Earth's crust Word Family Usage Examples Sign up, it's free! Whether you're a student, an educator, or a life-long learner, Vocabulary.com can put you on the path to systematic vocabulary improvement. | + | Which river flows through the town of Shrewsbury? | River Severn | river, Wales and England, United Kingdom | Britannica.com river, Wales and England, United Kingdom Written By: Wales River Severn, Welsh Hafren, Britain’s longest river from source to tidal waters—about 180 miles (290 km) long, with the Severn estuary adding some 40 miles (64 km) to its total length. The Severn rises near the River Wye on the northeastern slopes of Plynlimon (Welsh: Pumlumon), Wales , and follows a semicircular course basically southward to the Bristol Channel and the Atlantic Ocean . It drains an area of 4,350 square miles (11,266 square km) with an average discharge at Bewdley of 2,170 cubic feet (61.5 cubic m) per second. River Severn at Shrewsbury, Shropshire, Eng. Chris Bayley The river’s course is at first southeasterly, descending from an elevation of 2,000 feet (600 m) at its source to 500 feet (150 m) at the Welsh town of Llanidloes. There it turns sharply northeastward, following the Vale of Powys past Newtown and Welshpool . At Llanymynech the River Vyrnwy joins the Severn: the tributary headwaters are dammed to form the reservoir of Lake Vyrnwy, supplying Liverpool with drinking water. The enlarged Severn turns eastward over a plain on which it loops around the old town of Shrewsbury . Originally the river continued eastward to join the River Dee (which originates in North Wales and drains northward to the Irish Sea), but its course was blocked by ice during the Pleistocene Epoch, and its waters escaped to the southeast at Ironbridge . This course was maintained after deglaciation. The swiftly flowing current through the gorge at Ironbridge was important to the early iron industry of Coalbrookdale. Continuing southward, the Severn receives the River Stour at Stourport and passes through Worcester , where the cathedral stands on a cliff rising from the river’s steep left bank. The River Teme enters from the west below Worcester and the Avon from the northeast at Tewkesbury , a yachting and motorboat centre. At Gloucester the Severn becomes tidal and meanders to the sea. Navigation is difficult on this section and is bypassed by a ship canal (opened 1827), which leaves the estuary at Sharpness. Other canals that join the river, linking it with the Midlands region of England and with the River Thames , are virtually disused. The town of Bridgnorth, with the River Severn in the foreground, Shropshire, Eng. Pam Brophy The cathedral at Worcester, Hereford and Worcester, on a ridge above the River Severn. G.F. Allen/Bruce Coleman Inc. | + | Which band's name was inspired by a novel by Herman Hesse? | 23 Band Names Inspired by Literature :: Books :: Lists :: Paste 23 Band Names Inspired by Literature By Wyndham Wyeth  |  April 24, 2011  |  10:52pm Share Tweet Submit Pin At Paste, we look for “Signs of Life” in all forms of art. And while we value each artform for its unique merits, it’s always a treat when they overlap. So we decided to take a look at bands that derived their names from literature. The works that inspired several of the entries are probably obvious, but a few of them will most certainly surprise you. It may also surprise you to see which genres favor the written word. (Who knew metalheads were such scholars?) Photo by Max Blau New Jersey punks Titus Andronicus take their name from the greatest wordsmith of them all, William Shakespeare . Titus Andronicus is thought to be the famous playwright’s first tragedy. It is also his bloodiest and most violent work. 2. The Doors Source: The Doors of Perception by Aldous Huxley When The Doors formed in 1965, they decided to name themselves after Aldous Huxley’s book detailing the author’s experiences with taking mescaline. The Doors of Perception’s title was inspired by a William Blake quotation: “If the doors of perception were cleansed every thing would appear to man as it is, infinite.” 3. The Velvet Underground Source: The Velvet Underground by Michael Leigh Michael Leigh’s book about the secret sexual subculture of the early ‘60s became the inspiration for The Velvet Underground’s name when a friend of John Cale showed the book to the group. The band considered the name to be evocative of underground cinema. 4. Modest Mouse Source: “The Mark on the Wall” by Virginia Woolf Indie-rock outfit Modest Mouse derived their name from a passage in Virginia Woolf’s “The Mark on the Wall,” which reads “I wish I could hit upon a pleasant track of thought, a track indirectly reflecting credit upon myself, for those are the pleasantest thoughts, and very frequent even in the minds of modest, mouse-coloured people, who believe genuinely that they dislike to hear their own praises.” “I chose the name when I was fifteen,” frontman Issac Brock explains in Modest Mouse: A Pretty Good Read By Alan Goldsher. “I wanted something that was completely ambiguous , but it’s really candyesque sounding. But it meant something to me. And I could identify with that.” 5. Steely Dan Source: Naked Lunch by William S. Burroughs Bet you never knew about this one. The band’s name was taken from Steely Dan III from Yokohama, a strap-on dildo from William S. Burroughs’ non-linear narrative Naked Lunch. 6. Belle and Sebastian Source: Belle et Sébastien by Cécile Aubry Belle et Sébastien was a famous French novel about a boy and his dog living in a small French Alps mountain village. It spawned a French live-action television series in the 1965, a Japanese anime series in the ‘80s and the name of a popular indie-pop group in the ‘90s. 7. Esben and the Witch Source: Esben and the Witch – Danish Fairy Tale Three piece indie-rock band from Brighton, England Esben and the Witch takes its name from the Danish fairy tale about a boy’s encounters with a murderous witch. The name is fitting considering the dark tone of the band’s music. 8. Steppenwolf Source: Steppenwolf by Hermann Hesse Frontman John Kay decided to name his band after German-Swiss author Hermann Hesse’s 10th novel after a suggestion from Gabriel Mekler, the producer for the band’s debut album. In the book, the title refers to the protagonist’s low, animalistic nature represented as a “wolf of the steppes” 9. Veruca Salt Source: Charlie and the Chocolate Factory by Roald Dahl In Roald Dahl’s classic children’s book, Veruca Salt is a spoiled rich girl, whose bratty greed causes here to fall down an incinerator shaft. In 1993, Louise Post and Nina Gordon used the name for their alternative rock band. 10. Oryx and Crake Source: Oryx and Crake by Margaret Atwood Named after Margaret Atwood’s post-apocalyptic speculative fiction novel, Atlanta’s Oryx and Crake “offer lyrics that are influenced by both real life stories and overly active imag | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +### Evaluation Datasets + +#### gooaq + +* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) +* Size: 3,012,496 evaluation samples +* Columns: question and answer +* Approximate statistics based on the first 1000 samples: + | | question | answer | + |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | question | answer | + |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | how do i program my directv remote with my tv? | ['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.'] | + | are rodrigues fruit bats nocturnal? | Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night. | + | why does your heart rate increase during exercise bbc bitesize? | During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### msmarco + +* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) +* Size: 502,939 evaluation samples +* Columns: query, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | query | positive | negative | + |:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | query | positive | negative | + |:-------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | is cabinet refacing worth the cost? | Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From. | Most cabinet refacing projects cost about $4,000 to $10,000. The price varies based on the materials you select and the size and configuration of your kitchen. Wood veneer doors, for example, will cost less than solid wood doors. | + | is the fovea ethmoidalis a bone | Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a “cock's comb,” that projects intracranially and attaches to the falx cerebri. | Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a “cock's comb,” that projects intracranially and attaches to the falx cerebri. | + | average pitches per inning | The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work. | The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### squad + +* Dataset: [squad](https://huggingface.co/datasets/sentence-transformers/squad) at [d84c8c2](https://huggingface.co/datasets/sentence-transformers/squad/tree/d84c8c2ef64693264c890bb242d2e73fc0a46c40) +* Size: 87,599 evaluation samples +* Columns: question and answer +* Approximate statistics based on the first 1000 samples: + | | question | answer | + |:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | question | answer | + |:----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | When did the Russian Empire begin to question the existence of the Ottoman Empire? | In 1853 the Russian Empire on behalf of the Slavic Balkan states began to question the very existence of the Ottoman Empire. The result was the Crimean War, 1853–1856, in which the British Empire and the French Empire supported the Ottoman Empire in its struggle against the incursions of the Russian Empire. Eventually, the Ottoman Empire lost control of the Balkan region. | + | How would one describe the control of universities before nation-states in the 17th century? | The propagation of universities was not necessarily a steady progression, as the 17th century was rife with events that adversely affected university expansion. Many wars, and especially the Thirty Years' War, disrupted the university landscape throughout Europe at different times. War, plague, famine, regicide, and changes in religious power and structure often adversely affected the societies that provided support for universities. Internal strife within the universities themselves, such as student brawling and absentee professors, acted to destabilize these institutions as well. Universities were also reluctant to give up older curricula, and the continued reliance on the works of Aristotle defied contemporary advancements in science and the arts. This era was also affected by the rise of the nation-state. As universities increasingly came under state control, or formed under the auspices of the state, the faculty governance model (begun by the University of Paris) became more and more prominent. Although the older student-controlled universities still existed, they slowly started to move toward this structural organization. Control of universities still tended to be independent, although university leadership was increasingly appointed by the state. | + | When did Jewish law recognize copyright? | The concept's origins can potentially be traced back further. Jewish law includes several considerations whose effects are similar to those of modern intellectual property laws, though the notion of intellectual creations as property does not seem to exist – notably the principle of Hasagat Ge'vul (unfair encroachment) was used to justify limited-term publisher (but not author) copyright in the 16th century. In 500 BCE, the government of the Greek state of Sybaris offered one year's patent "to all who should discover any new refinement in luxury". | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### s2orc + +* Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) +* Size: 10,000 evaluation samples +* Columns: title and abstract +* Approximate statistics based on the first 1000 samples: + | | title | abstract | + |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | title | abstract | + |:-------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | Screen Printing Ink Film Thickness Analysis of the Passive RFID Tag Antenna | The relationship between the screen mesh and the theoretical and practical ink film thickness was analyzed based on the main influencing factors of the ink film thickness by screen printing.A calculation model for the ink thickness was established based on the screen under static and compressive deformation.The relation curve between the screen mesh and the ink film thickness was fitted and the suitable printing craft parameter was chosen to print two kinds of RFID tag antennas.The fluctuation of the antenna resistance was analyzed to demonstrate the reliability of the passive RFID tag antenna manufactured by screen printing technology. | + | Subclinical organ damage and cardiovascular risk prediction | AbstractTraditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage for risk discrimination, calibration and reclassification; and (ii) the economic costs and health ... | + | A Novel Approach to Simulate Climate Change Impacts on Vascular Epiphytes: Case Study in Taiwan | In the wet tropics, epiphytes form a conspicuous layer in the forest canopy, support abundant coexisting biota, and are known to have a critical influence on forest hydrology and nutrient cycling. Since canopy-dwelling plants have no vascular connection to the ground or their host plants, they are likely more sensitive to environmental changes than their soil-rooted counterparts, subsequently regarded as one of the groups most vulnerable to global climate change. Epiphytes have adapted to life in highly dynamic forest canopies by producing many, mostly wind-dispersed, seeds or spores. Consequently, epiphytes should colonize trees rapidly, which, in addition to atmospheric sensitivity and short life cycles, make epiphytes suitable climate change indicators. In this study, we assess the impact of climate change on Taiwanese epiphytes using a modeling approach. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### allnli + +* Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) +* Size: 6,584 evaluation samples +* Columns: anchor, positive, and negative +* Approximate statistics based on the first 1000 samples: + | | anchor | positive | negative | + |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | anchor | positive | negative | + |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| + | Two women are embracing while holding to go packages. | Two woman are holding packages. | The men are fighting outside a deli. | + | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | Two kids in jackets walk to school. | + | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | A woman drinks her coffee in a small cafe. | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### paq + +* Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) +* Size: 64,371,441 evaluation samples +* Columns: query and answer +* Approximate statistics based on the first 1000 samples: + | | query | answer | + |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | query | answer | + |:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | when did season 3 of the voice brasil start | The Voice Brasil (season 3) The third season of "The Voice Brasil", premiered on Rede Globo on September 18, 2014 in the 10:30 p.m. (BRT/AMT) slot immediately following the primetime telenovela "Império". The 22- and 24-year-old sertanejo duo Danilo Reis e Rafael won the competition on December 25, 2014 with 43% of the votes cast. This marked Lulu Santos' first win as a coach, the first stolen artist to win a Brazilian season of "The Voice", and the first time in any "The Voice" franchise that a duo won the competition. Online applications for "The Voice Brasil" were open on | + | when did the little ranger first come out | Gang" theme song was an instrumental medley of "London Bridge", "Here We Go Round the Mulberry Bush" and "The Farmer in the Dell". It remained in use until the series ended in 1944. The Little Ranger The Little Ranger is a 1938 "Our Gang" short comedy film directed by Gordon Douglas. It was the 169th short in the "Our Gang" series, and the first produced by Metro-Goldwyn-Mayer, who purchased the rights to the series from creator Hal Roach. Snubbed by his girlfriend Darla, Alfalfa accepts the invitation of tomboyish Muggsy to attend the local picture show. While watching the adventures | + | what is the name of rachel's sister in ninjaaiden | her among ten female characters who have never been featured on their games' cover arts, Samir Torres of VentureBeat wrote that while "Team Ninja sexualy exploits all of their female characters, yet Rachel somehow got axed from every modern "Ninja Gaiden" box art." Rachel (Ninja Gaiden) In 2004's "Ninja Gaiden", Rachel is a fiend hunter whom the game's protagonist Ryu Hayabusa meets in the Holy Vigoor Empire, where she is on a mission to destroy the fiends, as well as find her missing sister, Alma, who has become a Greater Fiend. Soon after they first meet, she is captured but | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +#### trivia_qa + +* Dataset: [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) +* Size: 73,346 evaluation samples +* Columns: query and answer +* Approximate statistics based on the first 1000 samples: + | | query | answer | + |:--------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | query | answer | + |:-----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | In which country is 'Ninety Mile Beach'? | Ninety (90) Mile Beach, Gippsland, Victoria - Tourism Australia Gippsland Find travel information on Ninety Mile Beach, one of the longest uninterrupted beaches in the world, located outside of Melbourne at Gippsland Lakes. Ninety Mile Beach, located in the Gippsland region on Victoria's south-eastern coastline, is one of the longest uninterrupted beaches in the world. Stand on the beach and watch the beach disappear into the salty sea spray in the distance. You might find that your footprints are the only ones in the sand that day. This is one of the most natural and unspoilt beaches in the world and is ideal for activities from beach fishing and swimming to walking, whale and dolphin-spotting or just lazing in the sun. Sun, sand and lush national parks all create the perfect holiday environment. Victoria's Ninety Mile Beach is a 90-mile long stretch of pristine golden sand that separates the Gippsland Lakes from Bass Strait. Stretching as far as the eye can see it is one of the most natural and unspoilt beaches in the world. It is also the third longest uninterrupted beach in the world. The beach sits at the edge of the Gippsland Lakes, the largest inland water system in the Southern Hemisphere. Woodside Beach is recognised as the start of the Ninety Mile Beach. The swimming beach is patrolled over summer and camping is available in the caravan park beside the beach. Adjacent to the Gippsland Lakes Coastal Park, Ninety Mile Beach Marine National Park covers five kilometres of coastline. The beach lies on the edge of a long slender sand dune and the absence of rocky outcrops or headlands results in an endless vista of sand and sea. Beneath the water, vast plains of sand stretch in every direction and provide a refuge for more species per square metre than most other marine habitats in the world. The park also has a rich indigenous history and is part of the country of the Gunaikurnai Aboriginal people. The charming seaside village of Lakes Entrance sits on the edge of Ninety Mile Beach where the Gippsland Lakes meets the Southern Ocean. Lakes Entrance is the perfect location for all types of water activities; but is especially renowned for its fishing. Try river fishing for bream, ocean beach fishing for open water fish, or launch a boat at one of the many ramps along the foreshore. The Ninety Mile Beach Surf Fishing Competition, Australia's largest surf fishing competition, is held at Ninety Mile Beach annually during late January. Loch Sport is another small town located east of Sale on a narrow spit of land between the sand dunes of Ninety Mile Beach and Lake Victoria. The shallow sandy beach of Lake Victoria at Loch Sport is a popular spot for swimming. Enjoy a picnic at Sperm Whale Head in The Lakes National Park, and watch out for dolphins and whales during the migration season. Take the Ninety Mile Beach Wildlife Trail and keep a lookout for kangaroos, emus and echidnas in and around the beach area. Along the trail you'll also encounter historic towns and villages, gourmet local produce, and local arts and crafts. Ninety Mile Beach is approximately 260 kilometres from Melbourne . More Holiday Ideas See what else there is near here to inspire your holiday planning. Gippsland | + | What country gets nearly 75% of its electricity from nuclear power? | Nuclear Power in France | French Nuclear Energy - World Nuclear Association Nuclear Power in France (Updated November 2016) France derives about 75% of its electricity from nuclear energy, due to a long-standing policy based on energy security. This share may be reduced to 50% by 2025. France is the world's largest net exporter of electricity due to its very low cost of generation, and gains over €3 billion per year from this. France has been very active in developing nuclear technology. Reactors and especially fuel products and services are a significant export. About 17% of France's electricity is from recycled nuclear fuel.     In 2014 French electricity generation was 541 TWh gross. Consumption in 2012 was 454 TWh – 6600 kWh per person. Winter demand varies by 2300 MWe per degree C. Over the last decade France has exported up to 70 billion kWh net each year and EdF expects exports to continue at 55-70 TWh/yr. In 2014 they were principally to Italy, UK, Switzerland, and Belgium, as well as to Spain. In 2014, net export was 65.1 TWh, in 2013 it was 48.5 billion kWh, and in 2012, 37.6 billion kWh. France has 58 nuclear reactors operated by Electricite de France (EdF), with total capacity of 63.2 GWe, supplying 416 billion kWh (net) in 2014, 77% of the total generated there (RTE data). Total generating capacity (end 2014, RTE data) is 129 GWe, including 25.4 GWe hydro, 24.4 GWe fossil fuel, 9.1 GWe wind and 5.3 GWe solar PV. Peak demand is about 100 GWe. In 2013 gross production was 424 TWh from nuclear, 76 billion kWh from hydro, 24.7 billion kWh from coal, 17.7 billion kWh from natural gas, 20.6 from solar and wind, and 8.0 from biofuels & waste, of total 575 TWh. The present situation is due to the French government deciding in 1974, just after the first oil shock, to expand rapidly the country's nuclear power capacity, using Westinghouse technology. This decision was taken in the context of France having substantial heavy engineering expertise but few known indigenous energy resources*. Nuclear energy, with the fuel cost being a relatively small part of the overall cost, made good sense in minimising imports and achieving greater energy security. * In 2014 the US EIA put French shale gas resources at 5094 billion m3, though recovery of this was prohibited. As a result of the 1974 decision, France now claims a substantial level of energy independence and almost the lowest cost electricity in Europe. It also has an extremely low level of CO2 emissions per capita from electricity generation, since over 90% of its electricity is nuclear or hydro. In mid-2010 a regular energy review of France by the International Energy Agency urged the country increasingly to take a strategic role as provider of low-cost, low-carbon base-load power for the whole of Europe rather than to concentrate on the energy independence which had driven policy since 1973. The low cost of French nuclear power generation is indicated by the national energy regulator (CRE) setting the price at which EdF’s electricity is sold to competing distributors. In 2014 the rate is €42/MWh, but CRE proposed an increase to €44 in 2015, €46 in 2016 and €48 in 2017 to allow EdF to recover costs of plant upgrades, which it put at €55 billion to extend all 58 reactor lifetimes by ten years. In November 2014 the government froze the price at €42 to mid-2015. This Arenh re-sale price has represented a long-term floor price for EdF’s power, and is nominally based on the cost of production. The industrial group Uniden said that the proposed 2015 wholesale price of €44/MWh would be €14 higher than Germany’s. French retail prices, without major effects from feed-in tariffs for wind and solar, remain very low. In 2013 French prices for medium-size industrials were about 90% of EU-27 average, and those for medium-size households (at less than 8 c/kWh) were less than half of EU-27 average. Recent energy policy In 1999 a parliamentary debate reaffirmed three main planks of French energy policy: security of supply (France imports more than half its energy), respect for the envir | + | Which Spaniard led an expedition which reached Tenochtitlan, the Aztec capital in 1519? | The Spanish Conquest (1519-1521) : Mexico History History  |  See all articles tagged history The Spanish Conquest (1519-1521) Tweet April 21, 1519--the year Ce Acatl (One Reed) by Aztec reckoning-- marked the opening of a short but decisive chapter in Mexico's history. On that day a fleet of 11 Spanish galleons sailing along the eastern gulf coast dropped anchor just off the wind-swept beach on the island of San Juan de Ulúa. Under the command of the wily, daring Hernán Cortés, the vessels bore 550 Spanish soldiers and sailors, as well as 16 horses, the first of the species to tread the American continent. The party disembarked to set up camp on the dunes behind the beach. In a friendly reception from the native Totonac Indians, greetings and gifts were exchanged. Cognizant of the existence of a great inland Empire, Cortés promptly dispatched a message requesting an audience with Aztec ruler Moctezuma II . (The term "Aztec" will be used throughout, although some historians prefer the less familiar designation "Mexica" for the last of Mexico's formidable pre-Hispanic civilizations.) Runners had already carried word to the "Lord of Cuhúa" in Tenochitlán, the capital city set on an island in Lake Texcoco some 200 hundred miles away. They reported the arrival of fair-skinned, bearded strangers and fearsome "man-beasts" (cavalry) who had descended from "towers floating on the sea." Cortés wasted no time in staking a claim for God and King, ceremoniously founding a settlement on the coast that he christened Villa Rica de la Vera Cruz, in reference to the fleet's arrival on Good Friday to what he believed to be a vast land of plenty. The Spanish Conquest had begun. All odds were against this tiny band of adventurers who would soon venture into unknown territory to topple the mighty Aztec Empire. It could never have happened were it not for Cortés' remarkable fortitude and cunning, coupled with an incredible series of coincidental prior events. In the wake the "discovery" of the Western Hemisphere by Christopher Columbus (1492), Spanish and Portuguese explorers continued the quest for riches in the New World. Among these were Francisco Hernández de Córdoba and Juan de Grijalba who, under the orders of Diego Veláquez, Spanish Governor of Cuba, set out on ill-fated ventures to the Yucatan and Mexico's gulf coast (1517-1518). Velázquez then commissioned the 34 year-old Cortés to lead a new expedition westward, but alarmed by escalating costs, had a last-minute change of heart. The eager and ever-astute Cortés eluded cancellation of the enterprise by hastily setting sail. The fleet first landed on the island of Cozumel off the Yucatan peninsula. There Cortés ransomed fellow Spaniard Gerónimo de Aguilar who had been forced to live among the Mayas after surviving a 1511 shipwreck during a prior expedition. Aguilar proved an invaluable asset to Cortés, acting as his personal interpreter of both native language and culture. Communication problems arose anew, however, as the Spaniards sailed farther north, encountering natives who spoke a different tongue. Fortuitously, the spoils of victory over a Tabascan chieftain at Potonchán included a gift of twenty native maidens, one of whom was fluent in both the Náhuatl and Mayan tongues. The comely and clever Malintzin was promptly baptized with a Spanish name, Marina, and appointed the task of intervening in further contacts with indigenous peoples. She translated Náhuatl to Mayan for Aguilar, who then put her words into Spanish. Doña Marina soon earned her place as Cortés' most intimate adviser by first mastering Spanish and then becoming his mistress. Eventually she bore him a son, Martín, the first mixed-blood Mexican or mestizo. For having aided the Spaniards, today she is widely considered a traitor to her own people. The moniker by which she is mostly commonly known, la Malinche, gave rise to the modern-day term malinchista used in reference a Mexican who takes a fancy to anything of foreign origin. Meanwhile, back in Tenochitlán, Moctezuma was in a quandary as to how to best deal with | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MultipleNegativesRankingLoss", + "matryoshka_dims": [ + 1024, + 512, + 256, + 128, + 64, + 32 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 2048 +- `per_device_eval_batch_size`: 2048 +- `learning_rate`: 0.2 +- `num_train_epochs`: 1 +- `warmup_ratio`: 0.1 +- `bf16`: True +- `batch_sampler`: no_duplicates + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 2048 +- `per_device_eval_batch_size`: 2048 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 1 +- `eval_accumulation_steps`: None +- `torch_empty_cache_steps`: None +- `learning_rate`: 0.2 +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 1 +- `max_steps`: -1 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.1 +- `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`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: False +- `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`: False +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `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 +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | gooaq loss | msmarco loss | squad loss | s2orc loss | allnli loss | paq loss | trivia qa loss | msmarco 10m loss | swim ir loss | pubmedqa loss | miracl loss | mldr loss | mr tydi 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 | 0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.0741 | 0.3518 | 0.2118 | 0.0793 | 0.3538 | 0.3200 | 0.1954 | 0.1589 | 0.6759 | 0.1532 | 0.0945 | 0.4296 | 0.1455 | 0.2495 | +| 0.0000 | 1 | 32.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0064 | 250 | 22.7851 | 8.3992 | 17.7191 | 17.6791 | 16.6296 | 18.7913 | 12.1404 | 18.9992 | 12.1891 | 11.6795 | 26.3440 | 8.5795 | 19.3571 | 9.5985 | 0.2366 | 0.5129 | 0.6004 | 0.1960 | 0.6334 | 0.3941 | 0.2713 | 0.3392 | 0.7977 | 0.2416 | 0.3819 | 0.5448 | 0.4679 | 0.4321 | +| 0.0127 | 500 | 9.6296 | 4.6987 | 13.6254 | 11.7605 | 12.5290 | 17.2038 | 6.9342 | 12.0873 | 7.3539 | 9.1374 | 23.1663 | 4.2482 | 14.5991 | 3.3365 | 0.2929 | 0.5509 | 0.6529 | 0.2890 | 0.6495 | 0.4244 | 0.2873 | 0.3690 | 0.8830 | 0.2373 | 0.3815 | 0.5802 | 0.5422 | 0.4723 | +| 0.0191 | 750 | 6.7008 | 3.6302 | 11.7061 | 10.1299 | 11.2366 | 15.1612 | 5.5833 | 10.6967 | 5.7074 | 8.8117 | 23.2404 | 3.5115 | 12.5734 | 2.4346 | 0.3101 | 0.5565 | 0.6684 | 0.3406 | 0.6354 | 0.4111 | 0.2972 | 0.3894 | 0.8611 | 0.2513 | 0.3613 | 0.5840 | 0.5578 | 0.4788 | +| 0.0255 | 1000 | 5.8282 | 2.9789 | 11.2050 | 9.5095 | 10.6029 | 14.7717 | 5.0173 | 9.6170 | 5.1146 | 9.0596 | 22.3746 | 3.0912 | 12.2982 | 2.2626 | 0.3066 | 0.5514 | 0.6654 | 0.3252 | 0.6390 | 0.4139 | 0.2917 | 0.4168 | 0.8678 | 0.2590 | 0.3884 | 0.6214 | 0.5614 | 0.4852 | +| 0.0318 | 1250 | 5.3975 | 2.8335 | 11.0393 | 9.3407 | 9.6014 | 14.5350 | 4.8262 | 9.4577 | 4.9009 | 8.9271 | 21.8053 | 3.2513 | 12.2634 | 1.6880 | 0.3090 | 0.5449 | 0.6607 | 0.3432 | 0.6243 | 0.4145 | 0.3026 | 0.4392 | 0.8801 | 0.2608 | 0.3760 | 0.6102 | 0.5768 | 0.4879 | +| 0.0382 | 1500 | 5.3077 | 2.7030 | 10.6366 | 8.9914 | 9.8588 | 14.6669 | 4.6253 | 9.3728 | 4.5863 | 9.1788 | 22.0617 | 2.8378 | 10.9618 | 1.8702 | 0.3240 | 0.5421 | 0.7026 | 0.3507 | 0.6227 | 0.4134 | 0.3150 | 0.3996 | 0.8776 | 0.2493 | 0.3625 | 0.6120 | 0.5642 | 0.4874 | +| 0.0445 | 1750 | 4.9354 | 2.6691 | 10.6339 | 8.9606 | 9.7095 | 14.9174 | 4.5880 | 9.3114 | 4.1786 | 8.2898 | 22.8332 | 2.6850 | 11.3781 | 1.6352 | 0.3092 | 0.5591 | 0.6615 | 0.3253 | 0.6363 | 0.3926 | 0.3165 | 0.4057 | 0.9019 | 0.2600 | 0.3685 | 0.6030 | 0.5563 | 0.4843 | +| 0.0509 | 2000 | 4.8017 | 2.5867 | 10.0547 | 8.8155 | 9.6765 | 14.7973 | 4.3931 | 9.2721 | 4.0193 | 7.7955 | 23.4468 | 1.9884 | 11.4315 | 1.8009 | 0.3274 | 0.5615 | 0.7024 | 0.3531 | 0.6481 | 0.3959 | 0.3134 | 0.4183 | 0.8849 | 0.2505 | 0.3694 | 0.5991 | 0.5664 | 0.4916 | +| 0.0573 | 2250 | 4.8193 | 2.4974 | 9.9855 | 8.8389 | 9.6763 | 14.4220 | 4.3112 | 9.1019 | 4.0176 | 8.4064 | 22.7034 | 2.7534 | 11.5256 | 2.3585 | 0.3149 | 0.5392 | 0.6689 | 0.3344 | 0.6495 | 0.4080 | 0.3058 | 0.3953 | 0.8857 | 0.2588 | 0.3426 | 0.5986 | 0.5756 | 0.4829 | +| 0.0636 | 2500 | 4.8773 | 2.7116 | 10.2180 | 8.6935 | 9.7664 | 14.3161 | 4.2722 | 8.9829 | 4.2454 | 9.1911 | 22.8367 | 2.6666 | 11.6110 | 2.0147 | 0.3377 | 0.5455 | 0.6547 | 0.3130 | 0.6396 | 0.4259 | 0.3256 | 0.4226 | 0.8825 | 0.2491 | 0.3908 | 0.5852 | 0.5656 | 0.4875 | +| 0.0700 | 2750 | 4.5856 | 2.7758 | 9.9754 | 8.6197 | 9.6282 | 14.4828 | 4.1534 | 8.8766 | 4.2312 | 9.5281 | 21.9368 | 2.9119 | 9.7259 | 1.9405 | 0.3384 | 0.5763 | 0.6646 | 0.3068 | 0.6740 | 0.4195 | 0.3155 | 0.4301 | 0.8809 | 0.2366 | 0.3965 | 0.5963 | 0.5714 | 0.4928 | +| 0.0764 | 3000 | 4.3725 | 2.5993 | 10.1291 | 8.7361 | 8.9502 | 14.8227 | 4.1163 | 8.8371 | 4.1014 | 9.2259 | 23.4047 | 3.2715 | 10.3051 | 2.3604 | 0.3040 | 0.5703 | 0.6836 | 0.3006 | 0.6355 | 0.3993 | 0.3318 | 0.4236 | 0.9001 | 0.2579 | 0.3966 | 0.5902 | 0.5770 | 0.4900 | +| 0.0827 | 3250 | 4.4409 | 2.5753 | 10.0879 | 8.5131 | 8.7106 | 14.8015 | 4.0560 | 8.8296 | 4.1868 | 9.3069 | 22.5793 | 2.3810 | 8.6639 | 2.0435 | 0.3147 | 0.5806 | 0.6766 | 0.3342 | 0.6293 | 0.4134 | 0.3208 | 0.4089 | 0.8834 | 0.2656 | 0.3784 | 0.6119 | 0.5731 | 0.4916 | +| 0.0891 | 3500 | 4.6192 | 2.4352 | 9.9932 | 8.5716 | 9.2016 | 14.1559 | 4.0585 | 8.9413 | 3.8278 | 8.6089 | 22.9941 | 2.5541 | 9.4271 | 1.7271 | 0.3052 | 0.5531 | 0.6921 | 0.3284 | 0.6391 | 0.4027 | 0.3288 | 0.4235 | 0.8938 | 0.2565 | 0.3928 | 0.5848 | 0.5741 | 0.4904 | +| 0.0955 | 3750 | 4.4805 | 2.5370 | 10.0723 | 8.4652 | 8.8024 | 14.4678 | 4.0045 | 8.8487 | 3.6855 | 8.4129 | 22.5177 | 2.5961 | 9.1362 | 1.6572 | 0.2996 | 0.5669 | 0.7051 | 0.3007 | 0.6433 | 0.3822 | 0.3127 | 0.4419 | 0.8853 | 0.2741 | 0.3696 | 0.5911 | 0.5796 | 0.4886 | +| 0.1018 | 4000 | 4.3246 | 2.4130 | 10.0235 | 8.4203 | 9.1794 | 14.4445 | 3.9667 | 8.8021 | 3.6692 | 8.0637 | 22.1590 | 2.2690 | 9.5487 | 1.4625 | 0.2987 | 0.5659 | 0.6905 | 0.3103 | 0.6280 | 0.4004 | 0.3025 | 0.4273 | 0.9026 | 0.2728 | 0.3710 | 0.6004 | 0.5746 | 0.4881 | +| 0.1082 | 4250 | 4.547 | 2.2318 | 10.0773 | 8.4970 | 8.6540 | 13.9845 | 3.9893 | 8.7805 | 3.4480 | 8.1038 | 21.2066 | 2.4344 | 9.2932 | 1.4761 | 0.3037 | 0.5760 | 0.7118 | 0.3239 | 0.6135 | 0.4056 | 0.3170 | 0.4323 | 0.8860 | 0.2620 | 0.3887 | 0.6211 | 0.5824 | 0.4941 | +| 0.1145 | 4500 | 4.3008 | 2.3243 | 9.8733 | 8.5042 | 9.0158 | 14.0935 | 3.9014 | 8.8306 | 3.5557 | 8.4240 | 21.2823 | 2.6280 | 9.4869 | 1.8310 | 0.3076 | 0.5788 | 0.6867 | 0.3187 | 0.6190 | 0.3936 | 0.3181 | 0.4160 | 0.8895 | 0.2564 | 0.3960 | 0.6148 | 0.5736 | 0.4899 | +| 0.1209 | 4750 | 4.2386 | 2.4259 | 9.8799 | 8.3964 | 9.1116 | 13.9412 | 3.8572 | 8.7955 | 3.6524 | 9.6881 | 21.3812 | 2.2282 | 8.9280 | 1.5408 | 0.3393 | 0.5783 | 0.7190 | 0.3137 | 0.6239 | 0.3953 | 0.3044 | 0.4231 | 0.8768 | 0.2636 | 0.3828 | 0.6043 | 0.5662 | 0.4916 | +| 0.1273 | 5000 | 4.141 | 2.4005 | 9.9973 | 8.2741 | 9.1627 | 14.4273 | 3.7931 | 8.7825 | 3.6856 | 9.0001 | 21.6595 | 2.2374 | 9.2771 | 1.4845 | 0.3243 | 0.5705 | 0.6858 | 0.3304 | 0.6328 | 0.3888 | 0.3145 | 0.4096 | 0.8775 | 0.2492 | 0.3769 | 0.6001 | 0.5600 | 0.4862 | +| 0.1336 | 5250 | 4.3221 | 2.4200 | 9.8792 | 8.2559 | 9.0431 | 13.9564 | 3.8055 | 8.5773 | 3.6137 | 8.1900 | 21.6272 | 2.2271 | 8.1229 | 1.6308 | 0.3207 | 0.5876 | 0.6945 | 0.3449 | 0.6232 | 0.4072 | 0.3011 | 0.4084 | 0.8894 | 0.2557 | 0.3668 | 0.5905 | 0.5582 | 0.4883 | +| 0.1400 | 5500 | 4.2121 | 2.3857 | 10.1277 | 8.3257 | 9.0878 | 13.8545 | 3.7696 | 8.6034 | 3.5613 | 8.7845 | 21.5562 | 2.3611 | 7.5145 | 1.9243 | 0.3160 | 0.5683 | 0.7024 | 0.3382 | 0.6244 | 0.4038 | 0.3075 | 0.4316 | 0.8784 | 0.2603 | 0.3876 | 0.5866 | 0.5650 | 0.4900 | +| 0.1464 | 5750 | 4.1071 | 2.4265 | 9.9555 | 8.0947 | 9.1289 | 14.0017 | 3.7337 | 8.6306 | 3.4562 | 8.3132 | 21.7894 | 2.1157 | 8.1967 | 1.5567 | 0.3317 | 0.5604 | 0.7101 | 0.3645 | 0.6460 | 0.3857 | 0.2987 | 0.4236 | 0.8830 | 0.2535 | 0.3867 | 0.5767 | 0.5612 | 0.4909 | +| 0.1527 | 6000 | 4.1189 | 2.3586 | 10.0799 | 8.0905 | 9.0291 | 14.0232 | 3.7064 | 8.5220 | 3.4742 | 8.3858 | 21.5903 | 2.1663 | 7.6242 | 1.4405 | 0.3264 | 0.5614 | 0.6825 | 0.3668 | 0.6296 | 0.3972 | 0.2863 | 0.4296 | 0.8869 | 0.2482 | 0.3809 | 0.6004 | 0.5556 | 0.4886 | +| 0.1591 | 6250 | 4.0873 | 2.2906 | 9.9813 | 8.1351 | 8.5907 | 13.8665 | 3.7028 | 8.5648 | 3.5042 | 8.1623 | 21.6688 | 2.2940 | 7.6652 | 1.5228 | 0.3444 | 0.5666 | 0.7035 | 0.3415 | 0.6188 | 0.3992 | 0.2989 | 0.4318 | 0.8816 | 0.2504 | 0.4014 | 0.6042 | 0.5637 | 0.4928 | +| 0.1654 | 6500 | 3.9586 | 2.2378 | 9.9318 | 8.0887 | 8.7977 | 14.1260 | 3.6614 | 8.5028 | 3.3178 | 8.3118 | 21.5718 | 2.2074 | 8.0905 | 1.7266 | 0.3299 | 0.5828 | 0.6994 | 0.3505 | 0.6375 | 0.3988 | 0.3225 | 0.4173 | 0.8891 | 0.2448 | 0.3930 | 0.6034 | 0.5614 | 0.4946 | +| 0.1718 | 6750 | 4.1981 | 2.2863 | 9.5475 | 7.6881 | 8.5462 | 13.6929 | 3.6704 | 8.6660 | 3.3401 | 8.9262 | 21.8933 | 2.0578 | 8.4832 | 1.6796 | 0.3456 | 0.5600 | 0.6983 | 0.3549 | 0.6445 | 0.3844 | 0.3139 | 0.4263 | 0.8952 | 0.2626 | 0.3877 | 0.5820 | 0.5597 | 0.4935 | +| 0.1782 | 7000 | 4.0528 | 2.3292 | 9.5129 | 7.8273 | 8.7743 | 13.6930 | 3.6284 | 8.6346 | 3.3430 | 8.5204 | 21.3129 | 2.3350 | 8.8695 | 1.9034 | 0.3457 | 0.5673 | 0.6850 | 0.3274 | 0.6321 | 0.3981 | 0.3171 | 0.4252 | 0.8830 | 0.2643 | 0.3901 | 0.5888 | 0.5590 | 0.4910 | +| 0.1845 | 7250 | 4.0547 | 2.2386 | 9.5373 | 7.9214 | 8.7896 | 13.6151 | 3.6172 | 8.5316 | 3.3128 | 9.3566 | 21.4568 | 2.3743 | 9.1696 | 1.7235 | 0.3528 | 0.5597 | 0.6931 | 0.3369 | 0.6327 | 0.3951 | 0.3111 | 0.4368 | 0.8787 | 0.2552 | 0.3758 | 0.5911 | 0.5515 | 0.4900 | +| 0.1909 | 7500 | 4.3005 | 2.2273 | 9.4397 | 7.9013 | 8.8606 | 13.3847 | 3.6401 | 8.4134 | 3.2583 | 8.3415 | 21.4206 | 2.4573 | 9.2348 | 1.4832 | 0.3557 | 0.5642 | 0.7145 | 0.3380 | 0.6412 | 0.3772 | 0.3085 | 0.4278 | 0.8792 | 0.2522 | 0.3738 | 0.5843 | 0.5587 | 0.4904 | +| 0.1973 | 7750 | 4.0054 | 2.2277 | 9.4653 | 7.9297 | 8.5999 | 13.6106 | 3.6049 | 8.3861 | 3.2335 | 9.3198 | 21.6595 | 2.4730 | 8.7335 | 1.6145 | 0.3396 | 0.5535 | 0.6901 | 0.3556 | 0.6311 | 0.3867 | 0.3182 | 0.4308 | 0.8692 | 0.2590 | 0.3654 | 0.5925 | 0.5558 | 0.4883 | +| 0.2036 | 8000 | 3.8426 | 2.2970 | 9.4352 | 7.9532 | 8.6501 | 13.9004 | 3.5835 | 8.3664 | 3.2109 | 8.4302 | 21.0340 | 2.1047 | 9.0103 | 1.1751 | 0.3420 | 0.5695 | 0.6868 | 0.3746 | 0.6434 | 0.4042 | 0.3193 | 0.4259 | 0.8847 | 0.2623 | 0.3785 | 0.5945 | 0.5702 | 0.4966 | +| 0.2100 | 8250 | 3.9404 | 2.2417 | 9.5349 | 7.8978 | 8.6899 | 13.8131 | 3.5697 | 8.3664 | 3.1548 | 8.6003 | 21.6214 | 2.0881 | 9.1829 | 0.9559 | 0.3306 | 0.5651 | 0.6959 | 0.3448 | 0.6455 | 0.3857 | 0.3123 | 0.4431 | 0.9009 | 0.2580 | 0.3981 | 0.6073 | 0.5748 | 0.4971 | +| 0.2164 | 8500 | 3.9522 | 2.2103 | 9.6575 | 7.9030 | 8.3617 | 14.0083 | 3.5433 | 8.3198 | 3.2148 | 8.5004 | 20.8166 | 2.3194 | 8.0428 | 1.2475 | 0.3343 | 0.5688 | 0.7054 | 0.3411 | 0.6625 | 0.3919 | 0.3148 | 0.4213 | 0.8965 | 0.2665 | 0.3454 | 0.6133 | 0.5782 | 0.4954 | +| 0.2227 | 8750 | 3.9665 | 2.1659 | 9.7216 | 7.8772 | 8.6394 | 13.9406 | 3.5289 | 8.3360 | 3.1863 | 8.7835 | 21.2033 | 2.1874 | 8.4683 | 1.2399 | 0.3231 | 0.5715 | 0.6794 | 0.3378 | 0.6684 | 0.3966 | 0.3171 | 0.4048 | 0.8887 | 0.2660 | 0.3461 | 0.6094 | 0.5559 | 0.4896 | +| 0.2291 | 9000 | 4.0217 | 2.1042 | 9.6765 | 7.8951 | 8.4255 | 13.8092 | 3.5314 | 8.3896 | 3.1097 | 8.0204 | 21.4246 | 2.0600 | 8.7244 | 1.3343 | 0.3218 | 0.5696 | 0.6931 | 0.3569 | 0.6654 | 0.3978 | 0.3159 | 0.4193 | 0.9000 | 0.2827 | 0.3750 | 0.5890 | 0.5796 | 0.4974 | +| 0.2354 | 9250 | 4.0008 | 2.0865 | 9.4154 | 7.9689 | 8.5298 | 13.6352 | 3.5371 | 8.4191 | 3.0414 | 8.4828 | 21.5173 | 1.9966 | 7.6465 | 1.1097 | 0.3282 | 0.5675 | 0.6934 | 0.3476 | 0.6559 | 0.3907 | 0.3272 | 0.4132 | 0.9038 | 0.2712 | 0.3891 | 0.5951 | 0.5716 | 0.4965 | +| 0.2418 | 9500 | 3.8041 | 2.0969 | 9.4478 | 7.9720 | 8.6298 | 13.7493 | 3.5003 | 8.4702 | 3.0939 | 8.5108 | 21.6929 | 1.9457 | 7.9947 | 1.2784 | 0.3291 | 0.5655 | 0.6915 | 0.3533 | 0.6495 | 0.3949 | 0.3291 | 0.4313 | 0.9007 | 0.2641 | 0.3910 | 0.5838 | 0.5710 | 0.4965 | +| 0.2482 | 9750 | 3.9483 | 2.1627 | 9.5085 | 7.8994 | 8.7048 | 13.4591 | 3.4941 | 8.3342 | 3.1202 | 8.7011 | 20.9101 | 1.8594 | 7.8214 | 1.1181 | 0.3312 | 0.5665 | 0.6870 | 0.3608 | 0.6530 | 0.4038 | 0.3267 | 0.4511 | 0.8973 | 0.2613 | 0.4063 | 0.5832 | 0.5660 | 0.4996 | +| 0.2545 | 10000 | 3.9843 | 2.1229 | 9.4348 | 7.9006 | 8.2377 | 13.5086 | 3.4905 | 8.2574 | 3.0114 | 8.3314 | 20.5157 | 1.9033 | 6.7485 | 1.1358 | 0.3365 | 0.5704 | 0.6858 | 0.3616 | 0.6561 | 0.3953 | 0.3263 | 0.4322 | 0.8833 | 0.2690 | 0.3995 | 0.6004 | 0.5691 | 0.4989 | +| 0.2609 | 10250 | 3.8779 | 2.1442 | 9.4343 | 7.9411 | 8.4677 | 13.5196 | 3.4558 | 8.2314 | 3.0592 | 8.6126 | 20.3879 | 1.9378 | 6.8287 | 1.1270 | 0.3192 | 0.5688 | 0.7051 | 0.3669 | 0.6577 | 0.3913 | 0.3167 | 0.4479 | 0.8978 | 0.2619 | 0.4030 | 0.5966 | 0.5699 | 0.5002 | +| 0.2673 | 10500 | 3.8708 | 2.1487 | 9.6030 | 7.9352 | 8.4657 | 13.3417 | 3.4574 | 8.3005 | 3.0167 | 8.4265 | 20.5354 | 1.9044 | 6.9217 | 1.1079 | 0.3255 | 0.5730 | 0.7020 | 0.3445 | 0.6527 | 0.3861 | 0.3178 | 0.4322 | 0.8859 | 0.2644 | 0.3997 | 0.5969 | 0.5739 | 0.4965 | +| 0.2736 | 10750 | 3.8153 | 2.1486 | 9.2440 | 7.8432 | 8.5271 | 13.4366 | 3.4702 | 8.3222 | 2.9680 | 8.4780 | 20.6855 | 1.8790 | 6.6261 | 1.0838 | 0.3240 | 0.5727 | 0.6970 | 0.3487 | 0.6546 | 0.3897 | 0.3294 | 0.4421 | 0.8908 | 0.2548 | 0.3802 | 0.6028 | 0.5744 | 0.4970 | +| 0.2800 | 11000 | 3.9693 | 2.1542 | 9.4072 | 7.8122 | 8.6226 | 13.1083 | 3.4426 | 8.2961 | 3.0086 | 8.6255 | 20.5001 | 1.9112 | 6.6305 | 1.1554 | 0.3273 | 0.5631 | 0.6912 | 0.3502 | 0.6431 | 0.4012 | 0.3173 | 0.4422 | 0.8834 | 0.2740 | 0.3829 | 0.6117 | 0.5745 | 0.4971 | +| 0.2864 | 11250 | 3.7596 | 2.1288 | 9.4115 | 7.8479 | 8.6295 | 13.1814 | 3.4282 | 8.2609 | 2.9531 | 8.6024 | 20.7827 | 1.8339 | 7.0503 | 1.0273 | 0.3301 | 0.5714 | 0.6814 | 0.3457 | 0.6447 | 0.3962 | 0.3142 | 0.4545 | 0.8751 | 0.2687 | 0.3827 | 0.5921 | 0.5492 | 0.4928 | +| 0.2927 | 11500 | 3.7377 | 2.1764 | 9.2284 | 7.7482 | 8.6753 | 13.2556 | 3.4186 | 8.2092 | 2.9631 | 8.2251 | 20.9522 | 1.8887 | 6.8783 | 1.1493 | 0.3184 | 0.5710 | 0.6896 | 0.3620 | 0.6411 | 0.4016 | 0.3141 | 0.4598 | 0.8871 | 0.2660 | 0.4035 | 0.5946 | 0.5680 | 0.4982 | +| 0.2991 | 11750 | 3.645 | 2.1757 | 9.2386 | 7.7988 | 8.4091 | 13.3105 | 3.3986 | 8.1770 | 3.0392 | 8.3319 | 20.8279 | 1.8464 | 7.2340 | 1.2369 | 0.3066 | 0.5680 | 0.6938 | 0.3413 | 0.6498 | 0.4035 | 0.3119 | 0.4692 | 0.8776 | 0.2697 | 0.4018 | 0.5937 | 0.5725 | 0.4969 | +| 0.3054 | 12000 | 3.8302 | 2.1730 | 9.2542 | 7.8031 | 8.3773 | 13.3083 | 3.4245 | 8.2024 | 2.9451 | 8.4451 | 20.3890 | 1.8992 | 7.1868 | 1.3149 | 0.3022 | 0.5639 | 0.7047 | 0.3580 | 0.6585 | 0.3953 | 0.3193 | 0.4512 | 0.8757 | 0.2667 | 0.4016 | 0.5815 | 0.5653 | 0.4957 | +| 0.3118 | 12250 | 3.7341 | 2.1580 | 9.1449 | 7.7487 | 8.2782 | 13.4871 | 3.4325 | 8.1531 | 2.8524 | 8.0765 | 20.4420 | 1.8084 | 7.4004 | 1.1942 | 0.3217 | 0.5648 | 0.7022 | 0.3658 | 0.6597 | 0.4010 | 0.3204 | 0.4470 | 0.8778 | 0.2668 | 0.4062 | 0.5841 | 0.5764 | 0.4995 | +| 0.3182 | 12500 | 3.6937 | 2.2003 | 9.0298 | 7.7776 | 8.3088 | 13.3345 | 3.4198 | 8.0772 | 2.8139 | 8.7163 | 20.4754 | 1.8802 | 7.2714 | 1.2016 | 0.3086 | 0.5676 | 0.6930 | 0.3609 | 0.6532 | 0.4067 | 0.3228 | 0.4341 | 0.8737 | 0.2723 | 0.4014 | 0.5783 | 0.5799 | 0.4964 | +| 0.3245 | 12750 | 3.6917 | 2.1808 | 9.0285 | 7.7770 | 8.3062 | 13.5737 | 3.3953 | 8.1291 | 2.8232 | 8.3426 | 20.9837 | 1.9420 | 7.1199 | 1.2568 | 0.3288 | 0.5620 | 0.6855 | 0.3512 | 0.6575 | 0.4069 | 0.3309 | 0.4372 | 0.8906 | 0.2709 | 0.4106 | 0.5928 | 0.5874 | 0.5009 | +| 0.3309 | 13000 | 3.6376 | 2.1621 | 9.0474 | 7.7871 | 8.1721 | 13.4874 | 3.3904 | 8.1507 | 2.8109 | 8.5607 | 21.0805 | 1.9734 | 7.0553 | 1.3466 | 0.3250 | 0.5686 | 0.6850 | 0.3520 | 0.6630 | 0.4044 | 0.3258 | 0.4424 | 0.8666 | 0.2684 | 0.4038 | 0.5769 | 0.5725 | 0.4965 | +| 0.3373 | 13250 | 3.7786 | 2.1146 | 9.1181 | 7.7333 | 8.2758 | 13.4782 | 3.3906 | 8.2021 | 2.8320 | 8.3097 | 21.1471 | 1.8529 | 7.3608 | 1.2242 | 0.3282 | 0.5649 | 0.6997 | 0.3761 | 0.6680 | 0.4097 | 0.3242 | 0.4143 | 0.8873 | 0.2784 | 0.3958 | 0.5956 | 0.5745 | 0.5013 | +| 0.3436 | 13500 | 3.8654 | 2.2053 | 9.0632 | 7.6973 | 8.4055 | 13.2312 | 3.3747 | 8.1627 | 2.8245 | 8.4075 | 20.2899 | 1.7553 | 7.1383 | 1.1577 | 0.3316 | 0.5672 | 0.6693 | 0.3901 | 0.6695 | 0.4017 | 0.3213 | 0.4138 | 0.8953 | 0.2703 | 0.4023 | 0.5856 | 0.5821 | 0.5000 | +| 0.3500 | 13750 | 3.7545 | 2.1424 | 9.0522 | 7.6998 | 8.3319 | 13.5322 | 3.3625 | 8.1303 | 2.8320 | 8.1860 | 20.4538 | 1.7997 | 7.0770 | 1.2512 | 0.3385 | 0.5654 | 0.6937 | 0.3856 | 0.6659 | 0.4025 | 0.3225 | 0.4156 | 0.8994 | 0.2675 | 0.4113 | 0.5886 | 0.5770 | 0.5026 | +| 0.3564 | 14000 | 3.715 | 2.1826 | 8.9396 | 7.6755 | 8.3111 | 13.3344 | 3.3331 | 8.1973 | 2.8760 | 8.8218 | 20.6306 | 1.9014 | 7.3386 | 1.2366 | 0.3256 | 0.5675 | 0.6903 | 0.3846 | 0.6706 | 0.4032 | 0.3300 | 0.4426 | 0.8876 | 0.2647 | 0.4082 | 0.5874 | 0.5770 | 0.5030 | +| 0.3627 | 14250 | 3.6348 | 2.1393 | 9.0032 | 7.7843 | 8.3942 | 13.2654 | 3.3368 | 8.1124 | 2.8874 | 8.5999 | 20.8261 | 1.8395 | 7.5311 | 1.1380 | 0.3339 | 0.5642 | 0.7175 | 0.3780 | 0.6600 | 0.3965 | 0.3229 | 0.4305 | 0.8915 | 0.2714 | 0.3914 | 0.5816 | 0.5732 | 0.5009 | +| 0.3691 | 14500 | 3.604 | 2.1709 | 8.9648 | 7.7166 | 8.4281 | 13.5507 | 3.3138 | 8.1362 | 2.8989 | 8.3940 | 20.6827 | 1.9298 | 7.3468 | 1.2705 | 0.3416 | 0.5594 | 0.7198 | 0.3776 | 0.6605 | 0.3965 | 0.3295 | 0.4341 | 0.8838 | 0.2632 | 0.3995 | 0.5884 | 0.5693 | 0.5018 | +| 0.3754 | 14750 | 3.5398 | 2.1451 | 9.0195 | 7.7407 | 8.4766 | 13.6169 | 3.2839 | 8.1386 | 2.9178 | 8.2585 | 21.1674 | 1.9484 | 7.5143 | 1.2747 | 0.3418 | 0.5592 | 0.6971 | 0.3763 | 0.6539 | 0.4060 | 0.3264 | 0.4214 | 0.8805 | 0.2524 | 0.3855 | 0.5872 | 0.5735 | 0.4970 | +| 0.3818 | 15000 | 3.7153 | 2.1038 | 8.9847 | 7.7279 | 8.2667 | 13.4033 | 3.2862 | 8.1232 | 2.8999 | 8.3224 | 20.9361 | 1.9569 | 7.3440 | 1.3036 | 0.3407 | 0.5687 | 0.7066 | 0.3497 | 0.6532 | 0.4043 | 0.3317 | 0.4300 | 0.8830 | 0.2564 | 0.3884 | 0.6013 | 0.5752 | 0.4992 | +| 0.3882 | 15250 | 3.752 | 2.0765 | 9.0293 | 7.7275 | 8.3168 | 13.2782 | 3.3105 | 8.0286 | 2.8404 | 8.2745 | 20.9582 | 1.7465 | 7.5436 | 1.2770 | 0.3313 | 0.5710 | 0.6945 | 0.3742 | 0.6699 | 0.4022 | 0.3327 | 0.4277 | 0.8903 | 0.2640 | 0.3872 | 0.5821 | 0.5662 | 0.4995 | +| 0.3945 | 15500 | 3.7794 | 2.0409 | 9.0264 | 7.7501 | 8.3903 | 13.3169 | 3.3058 | 8.0128 | 2.8463 | 8.5307 | 21.5663 | 1.7179 | 7.7448 | 1.1827 | 0.3286 | 0.5684 | 0.6961 | 0.3692 | 0.6743 | 0.4031 | 0.3267 | 0.4267 | 0.8927 | 0.2549 | 0.3777 | 0.5810 | 0.5686 | 0.4975 | +| 0.4009 | 15750 | 3.7444 | 2.0122 | 9.0629 | 7.7541 | 8.0134 | 13.2091 | 3.2956 | 8.0839 | 2.8528 | 8.1722 | 20.9378 | 1.7628 | 7.8655 | 1.2965 | 0.3282 | 0.5744 | 0.6839 | 0.3807 | 0.6644 | 0.4032 | 0.3277 | 0.4451 | 0.8896 | 0.2706 | 0.3916 | 0.5851 | 0.5685 | 0.5010 | +| 0.4073 | 16000 | 3.7817 | 2.0448 | 9.1787 | 7.7705 | 8.0529 | 13.1694 | 3.3128 | 8.1419 | 2.8104 | 8.2099 | 21.0454 | 1.7436 | 7.2934 | 1.2463 | 0.3137 | 0.5640 | 0.6884 | 0.3692 | 0.6600 | 0.3978 | 0.3215 | 0.4314 | 0.8937 | 0.2719 | 0.4094 | 0.6031 | 0.5697 | 0.4995 | +| 0.4136 | 16250 | 3.7293 | 2.0586 | 9.2379 | 7.7514 | 8.1877 | 13.1981 | 3.2983 | 8.0763 | 2.8564 | 8.6500 | 20.9279 | 1.8403 | 7.2051 | 1.1732 | 0.3405 | 0.5648 | 0.6944 | 0.3620 | 0.6614 | 0.3953 | 0.3286 | 0.4245 | 0.8921 | 0.2698 | 0.3946 | 0.5915 | 0.5770 | 0.4997 | +| 0.4200 | 16500 | 3.6243 | 2.0477 | 9.1718 | 7.6943 | 7.9493 | 13.3019 | 3.2908 | 8.0963 | 2.8306 | 8.7436 | 20.6790 | 1.8745 | 7.4356 | 1.1781 | 0.3272 | 0.5657 | 0.6944 | 0.3726 | 0.6848 | 0.3974 | 0.3318 | 0.4274 | 0.8939 | 0.2636 | 0.3948 | 0.5950 | 0.5746 | 0.5018 | +| 0.4263 | 16750 | 3.5071 | 2.0483 | 9.2054 | 7.7004 | 8.1887 | 13.3662 | 3.2727 | 7.9229 | 2.8256 | 8.2771 | 21.1584 | 1.8469 | 7.6476 | 1.2131 | 0.3318 | 0.5660 | 0.7000 | 0.3801 | 0.6902 | 0.3938 | 0.3224 | 0.4191 | 0.8969 | 0.2643 | 0.4052 | 0.6106 | 0.5684 | 0.5038 | +| 0.4327 | 17000 | 3.6337 | 2.0383 | 9.1228 | 7.7337 | 8.2262 | 13.2250 | 3.2714 | 7.9983 | 2.7662 | 8.4949 | 20.8407 | 1.8184 | 7.7876 | 1.2807 | 0.3251 | 0.5595 | 0.6957 | 0.3897 | 0.6697 | 0.3933 | 0.3123 | 0.4334 | 0.8934 | 0.2663 | 0.3935 | 0.5931 | 0.5700 | 0.4996 | +| 0.4391 | 17250 | 3.5075 | 2.0327 | 8.9777 | 7.7194 | 8.2392 | 13.4002 | 3.2678 | 7.9239 | 2.7551 | 8.2470 | 21.1674 | 1.7744 | 7.9402 | 1.3115 | 0.3418 | 0.5595 | 0.7123 | 0.3790 | 0.6625 | 0.3978 | 0.3174 | 0.4232 | 0.8866 | 0.2683 | 0.3926 | 0.5876 | 0.5764 | 0.5004 | +| 0.4454 | 17500 | 3.6595 | 2.0419 | 8.9509 | 7.6536 | 8.3099 | 13.3537 | 3.2766 | 7.9939 | 2.7604 | 8.3880 | 20.8993 | 1.8358 | 7.6156 | 1.2238 | 0.3311 | 0.5643 | 0.7049 | 0.3564 | 0.6686 | 0.3932 | 0.3099 | 0.4350 | 0.8872 | 0.2687 | 0.3826 | 0.5854 | 0.5665 | 0.4964 | +| 0.4518 | 17750 | 3.5743 | 2.0049 | 9.0019 | 7.6693 | 8.3489 | 13.3261 | 3.2758 | 8.0051 | 2.7881 | 8.4247 | 20.8115 | 1.8714 | 7.7491 | 1.1884 | 0.3341 | 0.5614 | 0.6972 | 0.3571 | 0.6625 | 0.3893 | 0.3036 | 0.4368 | 0.8911 | 0.2625 | 0.3861 | 0.5734 | 0.5681 | 0.4941 | +| 0.4582 | 18000 | 3.6038 | 1.9810 | 9.0106 | 7.7162 | 8.3584 | 13.1195 | 3.2674 | 8.0266 | 2.8184 | 8.4383 | 20.5199 | 1.8854 | 7.8663 | 1.1762 | 0.3318 | 0.5583 | 0.7086 | 0.3591 | 0.6509 | 0.3878 | 0.3137 | 0.4247 | 0.8831 | 0.2601 | 0.3921 | 0.5978 | 0.5648 | 0.4948 | +| 0.4645 | 18250 | 3.6903 | 2.0005 | 9.0187 | 7.6743 | 8.4280 | 13.0108 | 3.2733 | 7.8845 | 2.7810 | 8.3511 | 20.1457 | 1.7802 | 8.0015 | 1.0885 | 0.3407 | 0.5657 | 0.7033 | 0.3626 | 0.6644 | 0.3841 | 0.3299 | 0.4358 | 0.8844 | 0.2642 | 0.3904 | 0.5871 | 0.5632 | 0.4981 | +| 0.4709 | 18500 | 3.7208 | 1.9972 | 9.1020 | 7.6472 | 8.1589 | 13.0717 | 3.2601 | 8.0039 | 2.7673 | 8.3361 | 20.0231 | 1.8054 | 7.7381 | 1.1832 | 0.3328 | 0.5652 | 0.7043 | 0.3473 | 0.6693 | 0.3810 | 0.3313 | 0.4293 | 0.8770 | 0.2633 | 0.3946 | 0.5914 | 0.5634 | 0.4962 | +| 0.4773 | 18750 | 3.6357 | 2.0069 | 9.1473 | 7.6843 | 8.2110 | 13.1578 | 3.2540 | 7.9856 | 2.7390 | 8.6913 | 20.3263 | 1.8252 | 7.9545 | 1.0354 | 0.3285 | 0.5631 | 0.7093 | 0.3648 | 0.6685 | 0.3842 | 0.3285 | 0.4361 | 0.8918 | 0.2744 | 0.4065 | 0.5814 | 0.5610 | 0.4998 | +| 0.4836 | 19000 | 3.5737 | 1.9755 | 9.1397 | 7.6784 | 8.2604 | 13.3462 | 3.2391 | 7.9876 | 2.7643 | 8.4540 | 20.2047 | 1.7528 | 7.6572 | 1.0906 | 0.3284 | 0.5631 | 0.7083 | 0.3657 | 0.6660 | 0.3795 | 0.3186 | 0.4393 | 0.8876 | 0.2613 | 0.4056 | 0.5835 | 0.5637 | 0.4977 | +| 0.4900 | 19250 | 3.5325 | 2.0232 | 9.1987 | 7.6517 | 8.2727 | 13.1941 | 3.2276 | 7.9841 | 2.7238 | 8.4698 | 19.9076 | 1.8015 | 7.2144 | 1.1134 | 0.3337 | 0.5635 | 0.7076 | 0.3570 | 0.6572 | 0.3891 | 0.3297 | 0.4360 | 0.8864 | 0.2689 | 0.3997 | 0.5813 | 0.5608 | 0.4978 | +| 0.4963 | 19500 | 3.4782 | 2.0033 | 9.1235 | 7.7026 | 8.3383 | 13.1817 | 3.2308 | 8.0122 | 2.6934 | 8.4448 | 19.7121 | 1.7192 | 6.9417 | 0.9934 | 0.3222 | 0.5656 | 0.7009 | 0.3562 | 0.6654 | 0.3792 | 0.3292 | 0.4544 | 0.8803 | 0.2703 | 0.4027 | 0.5892 | 0.5611 | 0.4982 | +| 0.5027 | 19750 | 3.7141 | 1.9830 | 9.2166 | 7.6724 | 8.3188 | 13.1514 | 3.2465 | 8.0979 | 2.7005 | 8.1956 | 19.8289 | 1.6910 | 7.1995 | 1.0668 | 0.3323 | 0.5632 | 0.7209 | 0.3442 | 0.6745 | 0.3864 | 0.3287 | 0.4255 | 0.8828 | 0.2751 | 0.4097 | 0.5785 | 0.5597 | 0.4986 | +| 0.5091 | 20000 | 3.7058 | 1.9625 | 9.1729 | 7.6554 | 8.3323 | 13.0269 | 3.2368 | 8.0075 | 2.7159 | 8.6974 | 20.1650 | 1.6862 | 7.3387 | 1.1706 | 0.3414 | 0.5637 | 0.7214 | 0.3498 | 0.6745 | 0.3817 | 0.3309 | 0.4290 | 0.8861 | 0.2711 | 0.3861 | 0.5854 | 0.5757 | 0.4998 | +| 0.5154 | 20250 | 3.502 | 1.9983 | 9.1304 | 7.6354 | 8.3832 | 13.2376 | 3.2262 | 7.9229 | 2.7119 | 8.7638 | 20.0759 | 1.6633 | 6.9686 | 1.1490 | 0.3296 | 0.5703 | 0.7116 | 0.3409 | 0.6717 | 0.3847 | 0.3294 | 0.4198 | 0.8859 | 0.2655 | 0.3924 | 0.5836 | 0.5647 | 0.4962 | +| 0.5218 | 20500 | 3.6424 | 1.9867 | 9.0935 | 7.6470 | 8.4280 | 13.0866 | 3.2338 | 7.9608 | 2.7225 | 8.4571 | 20.1605 | 1.6184 | 6.8877 | 1.1519 | 0.3377 | 0.5682 | 0.7128 | 0.3479 | 0.6646 | 0.3900 | 0.3255 | 0.4408 | 0.8863 | 0.2655 | 0.4184 | 0.5862 | 0.5627 | 0.5005 | +| 0.5282 | 20750 | 3.6085 | 1.9589 | 9.1559 | 7.6282 | 8.3675 | 13.1289 | 3.2393 | 8.0050 | 2.7199 | 8.3705 | 20.4945 | 1.6721 | 6.8649 | 1.2063 | 0.3328 | 0.5719 | 0.7054 | 0.3526 | 0.6755 | 0.3974 | 0.3280 | 0.4204 | 0.8926 | 0.2686 | 0.4145 | 0.5783 | 0.5553 | 0.4995 | +| 0.5345 | 21000 | 3.5763 | 1.9392 | 9.1139 | 7.6917 | 8.2745 | 13.3345 | 3.2333 | 7.9848 | 2.7010 | 8.4815 | 20.5463 | 1.7049 | 7.0751 | 1.1276 | 0.3251 | 0.5710 | 0.7128 | 0.3561 | 0.6642 | 0.4022 | 0.3225 | 0.4394 | 0.8997 | 0.2737 | 0.4106 | 0.5732 | 0.5592 | 0.5008 | +| 0.5409 | 21250 | 3.5401 | 1.9744 | 9.0583 | 7.5955 | 8.2868 | 13.2877 | 3.2309 | 7.9686 | 2.6641 | 8.2829 | 20.3974 | 1.6843 | 6.9287 | 1.0128 | 0.3322 | 0.5692 | 0.7117 | 0.3348 | 0.6715 | 0.3834 | 0.3269 | 0.4372 | 0.8869 | 0.2662 | 0.4097 | 0.5797 | 0.5587 | 0.4975 | +| 0.5473 | 21500 | 3.489 | 1.9417 | 8.9543 | 7.6468 | 8.3612 | 13.3847 | 3.2314 | 7.9631 | 2.6635 | 8.4673 | 20.5367 | 1.7459 | 6.7037 | 1.0989 | 0.3328 | 0.5741 | 0.7097 | 0.3508 | 0.6660 | 0.3916 | 0.3277 | 0.4391 | 0.8860 | 0.2632 | 0.4100 | 0.5910 | 0.5578 | 0.5000 | +| 0.5536 | 21750 | 3.555 | 1.9533 | 8.9916 | 7.6360 | 8.3586 | 13.3764 | 3.2284 | 7.9575 | 2.7214 | 8.3429 | 20.5891 | 1.7569 | 6.6890 | 1.1157 | 0.3339 | 0.5744 | 0.7114 | 0.3392 | 0.6636 | 0.3964 | 0.3259 | 0.4504 | 0.8925 | 0.2651 | 0.4031 | 0.5792 | 0.5646 | 0.5000 | +| 0.5600 | 22000 | 3.586 | 1.9301 | 8.9806 | 7.6738 | 8.3535 | 13.2453 | 3.2316 | 7.9946 | 2.6907 | 8.2869 | 20.5606 | 1.6573 | 6.8912 | 1.1068 | 0.3239 | 0.5788 | 0.7121 | 0.3537 | 0.6589 | 0.4023 | 0.3243 | 0.4499 | 0.8891 | 0.2644 | 0.4083 | 0.5802 | 0.5616 | 0.5006 | +| 0.5663 | 22250 | 3.5084 | 1.9406 | 9.0040 | 7.6810 | 8.3815 | 13.1792 | 3.2167 | 7.9700 | 2.7316 | 8.6409 | 20.6425 | 1.6145 | 6.8099 | 1.1404 | 0.3174 | 0.5772 | 0.7177 | 0.3519 | 0.6613 | 0.3950 | 0.3324 | 0.4362 | 0.8894 | 0.2648 | 0.4173 | 0.5857 | 0.5626 | 0.5007 | +| 0.5727 | 22500 | 3.5095 | 1.9202 | 8.9619 | 7.6717 | 8.3825 | 13.1359 | 3.2147 | 7.9776 | 2.7114 | 8.2541 | 20.5107 | 1.6855 | 6.9227 | 1.1935 | 0.3335 | 0.5726 | 0.7134 | 0.3574 | 0.6578 | 0.3959 | 0.3262 | 0.4401 | 0.8968 | 0.2604 | 0.4105 | 0.5826 | 0.5620 | 0.5007 | +| 0.5791 | 22750 | 3.5059 | 1.9225 | 8.9956 | 7.6775 | 8.3968 | 13.0329 | 3.2114 | 7.9836 | 2.7107 | 8.4843 | 20.6793 | 1.6821 | 7.0987 | 1.0579 | 0.3273 | 0.5703 | 0.7118 | 0.3689 | 0.6490 | 0.3850 | 0.3257 | 0.4320 | 0.8824 | 0.2622 | 0.4119 | 0.5898 | 0.5605 | 0.4982 | +| 0.5854 | 23000 | 3.4047 | 1.9716 | 9.0017 | 7.6435 | 8.4379 | 13.0467 | 3.1957 | 7.9843 | 2.7017 | 8.5995 | 20.8783 | 1.5818 | 7.1997 | 1.0067 | 0.3272 | 0.5718 | 0.7132 | 0.3728 | 0.6544 | 0.3913 | 0.3255 | 0.4377 | 0.8869 | 0.2583 | 0.4107 | 0.5916 | 0.5539 | 0.4996 | +| 0.5918 | 23250 | 3.4732 | 1.9518 | 8.9827 | 7.6143 | 8.3925 | 13.2800 | 3.1877 | 7.9616 | 2.7122 | 8.5013 | 21.0376 | 1.6291 | 7.0831 | 1.0816 | 0.3316 | 0.5689 | 0.7129 | 0.3705 | 0.6536 | 0.3847 | 0.3211 | 0.4495 | 0.8844 | 0.2622 | 0.4141 | 0.5916 | 0.5551 | 0.5000 | +| 0.5982 | 23500 | 3.4271 | 1.9688 | 9.0092 | 7.5830 | 8.3763 | 13.2765 | 3.1857 | 7.9625 | 2.6794 | 8.4899 | 20.8080 | 1.6519 | 7.1604 | 1.1423 | 0.3346 | 0.5716 | 0.7054 | 0.3637 | 0.6562 | 0.3844 | 0.3249 | 0.4346 | 0.8912 | 0.2577 | 0.4009 | 0.5872 | 0.5697 | 0.4986 | +| 0.6045 | 23750 | 3.4701 | 2.0238 | 9.0036 | 7.5173 | 8.4058 | 13.2881 | 3.1791 | 7.9275 | 2.7149 | 8.8465 | 20.6630 | 1.7025 | 7.2286 | 1.1973 | 0.3308 | 0.5670 | 0.7040 | 0.3754 | 0.6609 | 0.3936 | 0.3252 | 0.4475 | 0.8948 | 0.2542 | 0.4062 | 0.5877 | 0.5717 | 0.5015 | +| 0.6109 | 24000 | 3.6199 | 2.0084 | 8.9869 | 7.5146 | 8.3790 | 13.1350 | 3.1771 | 7.9137 | 2.7032 | 8.5424 | 20.5441 | 1.7652 | 6.8017 | 1.1617 | 0.3224 | 0.5656 | 0.7045 | 0.3643 | 0.6643 | 0.3864 | 0.3174 | 0.4577 | 0.8873 | 0.2571 | 0.3735 | 0.5939 | 0.5611 | 0.4966 | +| 0.6173 | 24250 | 3.408 | 2.0137 | 9.0355 | 7.5305 | 8.3597 | 13.1604 | 3.1735 | 7.9201 | 2.7078 | 9.0787 | 20.5226 | 1.7341 | 6.7525 | 1.0237 | 0.3310 | 0.5676 | 0.7061 | 0.3643 | 0.6602 | 0.3995 | 0.3240 | 0.4588 | 0.8936 | 0.2572 | 0.3868 | 0.5928 | 0.5718 | 0.5011 | +| 0.6236 | 24500 | 3.4651 | 1.9564 | 8.9636 | 7.5635 | 8.3731 | 13.3261 | 3.1737 | 7.9529 | 2.6638 | 8.5231 | 20.4787 | 1.7792 | 6.7317 | 1.0516 | 0.3291 | 0.5670 | 0.7060 | 0.3572 | 0.6594 | 0.3982 | 0.3253 | 0.4400 | 0.8917 | 0.2577 | 0.3886 | 0.5951 | 0.5662 | 0.4986 | +| 0.6300 | 24750 | 3.6988 | 1.9668 | 8.9583 | 7.5875 | 8.4316 | 12.9644 | 3.1769 | 7.9801 | 2.6750 | 8.5780 | 20.3883 | 1.7045 | 6.9010 | 1.0783 | 0.3300 | 0.5668 | 0.7040 | 0.3653 | 0.6550 | 0.3957 | 0.3293 | 0.4570 | 0.8878 | 0.2621 | 0.3816 | 0.5932 | 0.5665 | 0.4996 | +| 0.6363 | 25000 | 3.4365 | 1.9782 | 8.9306 | 7.5829 | 8.4123 | 13.0937 | 3.1640 | 7.9870 | 2.6806 | 8.6281 | 20.2045 | 1.7244 | 6.9685 | 1.0365 | 0.3289 | 0.5651 | 0.6956 | 0.3703 | 0.6494 | 0.4025 | 0.3294 | 0.4511 | 0.8841 | 0.2634 | 0.3894 | 0.5970 | 0.5605 | 0.4990 | +| 0.6427 | 25250 | 3.6097 | 1.9653 | 8.9386 | 7.5722 | 8.4185 | 13.0207 | 3.1629 | 7.9910 | 2.6867 | 8.7326 | 20.2092 | 1.7321 | 6.8303 | 1.0569 | 0.3273 | 0.5643 | 0.7024 | 0.3587 | 0.6534 | 0.4018 | 0.3282 | 0.4401 | 0.8872 | 0.2614 | 0.3860 | 0.5966 | 0.5703 | 0.4983 | +| 0.6491 | 25500 | 3.5379 | 1.9518 | 8.9189 | 7.5241 | 8.4156 | 13.0924 | 3.1557 | 7.9747 | 2.6468 | 8.6005 | 20.3848 | 1.7474 | 6.9006 | 1.0193 | 0.3186 | 0.5692 | 0.7032 | 0.3581 | 0.6429 | 0.4068 | 0.3288 | 0.4426 | 0.8937 | 0.2584 | 0.4055 | 0.5963 | 0.5667 | 0.4993 | +| 0.6554 | 25750 | 3.6223 | 1.9609 | 8.9459 | 7.5554 | 8.4227 | 12.8817 | 3.1483 | 7.9813 | 2.6559 | 8.8423 | 20.3294 | 1.6381 | 6.8535 | 1.0140 | 0.3252 | 0.5722 | 0.7104 | 0.3689 | 0.6554 | 0.4087 | 0.3291 | 0.4293 | 0.8894 | 0.2632 | 0.3940 | 0.5995 | 0.5702 | 0.5012 | +| 0.6618 | 26000 | 3.402 | 1.9602 | 8.9366 | 7.5236 | 8.2409 | 12.9294 | 3.1424 | 7.9619 | 2.6323 | 8.6640 | 20.2959 | 1.6627 | 6.7331 | 1.0115 | 0.3276 | 0.5676 | 0.7031 | 0.3652 | 0.6493 | 0.4030 | 0.3263 | 0.4298 | 0.8830 | 0.2655 | 0.4030 | 0.6017 | 0.5724 | 0.4998 | +| 0.6682 | 26250 | 3.4876 | 1.9929 | 8.9621 | 7.4947 | 8.1977 | 12.8814 | 3.1350 | 7.8663 | 2.6554 | 8.5629 | 20.3156 | 1.7019 | 6.7602 | 0.9582 | 0.3253 | 0.5639 | 0.7127 | 0.3621 | 0.6544 | 0.4146 | 0.3278 | 0.4350 | 0.8895 | 0.2613 | 0.4056 | 0.5997 | 0.5635 | 0.5012 | +| 0.6745 | 26500 | 3.4301 | 1.9752 | 8.9474 | 7.5246 | 8.1379 | 12.7941 | 3.1369 | 7.8999 | 2.6256 | 8.5223 | 20.4417 | 1.6627 | 6.7999 | 0.9626 | 0.3232 | 0.5661 | 0.7094 | 0.3719 | 0.6474 | 0.4077 | 0.3299 | 0.4533 | 0.8849 | 0.2620 | 0.3993 | 0.6008 | 0.5665 | 0.5017 | +| 0.6809 | 26750 | 3.482 | 1.9646 | 8.9353 | 7.5199 | 8.2066 | 12.6512 | 3.1284 | 7.9080 | 2.6464 | 8.6207 | 20.4063 | 1.6852 | 6.7490 | 1.0089 | 0.3172 | 0.5689 | 0.7121 | 0.3734 | 0.6461 | 0.4089 | 0.3287 | 0.4516 | 0.8854 | 0.2601 | 0.3965 | 0.5993 | 0.5634 | 0.5009 | +| 0.6873 | 27000 | 3.5073 | 1.9431 | 8.9478 | 7.5188 | 8.0549 | 12.6486 | 3.1310 | 7.9241 | 2.6508 | 8.5172 | 20.4459 | 1.6855 | 6.8390 | 1.0012 | 0.3222 | 0.5705 | 0.6936 | 0.3675 | 0.6511 | 0.3970 | 0.3286 | 0.4467 | 0.8875 | 0.2587 | 0.3986 | 0.6177 | 0.5690 | 0.5007 | +| 0.6936 | 27250 | 3.5565 | 1.9438 | 8.9610 | 7.5044 | 8.0383 | 12.4879 | 3.1277 | 7.9219 | 2.6321 | 8.4003 | 20.6229 | 1.7421 | 6.7256 | 1.0533 | 0.3327 | 0.5737 | 0.6955 | 0.3653 | 0.6486 | 0.4067 | 0.3277 | 0.4368 | 0.8901 | 0.2589 | 0.3877 | 0.6135 | 0.5696 | 0.5005 | +| 0.7000 | 27500 | 3.4506 | 1.9639 | 8.9673 | 7.4644 | 8.1455 | 12.4523 | 3.1171 | 7.9159 | 2.6772 | 8.5339 | 20.7734 | 1.7690 | 6.6677 | 1.0708 | 0.3260 | 0.5730 | 0.6960 | 0.3562 | 0.6419 | 0.4053 | 0.3310 | 0.4395 | 0.8871 | 0.2594 | 0.4010 | 0.6155 | 0.5679 | 0.5000 | +| 0.7063 | 27750 | 3.4875 | 1.9177 | 8.9276 | 7.4754 | 8.1375 | 12.5163 | 3.1315 | 7.8697 | 2.6247 | 8.4426 | 20.4950 | 1.7047 | 6.6303 | 1.0030 | 0.3244 | 0.5690 | 0.6887 | 0.3499 | 0.6471 | 0.4057 | 0.3309 | 0.4464 | 0.8893 | 0.2570 | 0.4038 | 0.6038 | 0.5701 | 0.4989 | +| 0.7127 | 28000 | 3.5298 | 1.8939 | 8.9022 | 7.4889 | 8.1322 | 12.5461 | 3.1409 | 7.8621 | 2.5963 | 8.4173 | 20.4948 | 1.7078 | 6.4516 | 0.9874 | 0.3380 | 0.5721 | 0.6934 | 0.3631 | 0.6439 | 0.4046 | 0.3272 | 0.4486 | 0.8893 | 0.2593 | 0.4020 | 0.6017 | 0.5687 | 0.5009 | +| 0.7191 | 28250 | 3.3329 | 1.8970 | 8.9172 | 7.5001 | 8.1758 | 12.5515 | 3.1333 | 7.9075 | 2.5928 | 8.5025 | 20.3447 | 1.6879 | 6.5795 | 0.9658 | 0.3247 | 0.5729 | 0.7000 | 0.3710 | 0.6468 | 0.4069 | 0.3336 | 0.4564 | 0.8933 | 0.2637 | 0.4105 | 0.6012 | 0.5749 | 0.5043 | +| 0.7254 | 28500 | 3.3897 | 1.8966 | 8.9038 | 7.5265 | 8.2199 | 12.6173 | 3.1244 | 7.8882 | 2.5974 | 8.3728 | 20.3628 | 1.6804 | 6.6887 | 0.9733 | 0.3278 | 0.5750 | 0.7001 | 0.3645 | 0.6528 | 0.4145 | 0.3310 | 0.4669 | 0.8964 | 0.2638 | 0.4032 | 0.6086 | 0.5713 | 0.5058 | +| 0.7318 | 28750 | 3.4588 | 1.8934 | 8.8948 | 7.5026 | 8.2374 | 12.4866 | 3.1287 | 7.8941 | 2.5811 | 8.3781 | 20.4334 | 1.7109 | 6.6195 | 0.9699 | 0.3251 | 0.5707 | 0.7083 | 0.3702 | 0.6411 | 0.4086 | 0.3252 | 0.4553 | 0.8935 | 0.2641 | 0.4068 | 0.6063 | 0.5592 | 0.5026 | +| 0.7382 | 29000 | 3.3675 | 1.8959 | 8.8925 | 7.5043 | 8.2628 | 12.6063 | 3.1174 | 7.9132 | 2.5908 | 8.2436 | 20.3771 | 1.6740 | 6.7151 | 0.9895 | 0.3262 | 0.5725 | 0.7069 | 0.3694 | 0.6495 | 0.4063 | 0.3169 | 0.4622 | 0.8962 | 0.2609 | 0.4124 | 0.6101 | 0.5635 | 0.5041 | +| 0.7445 | 29250 | 3.3886 | 1.8976 | 8.9215 | 7.4975 | 8.2401 | 12.4978 | 3.1127 | 7.8615 | 2.5814 | 8.2715 | 20.4379 | 1.6740 | 6.7705 | 0.9492 | 0.3269 | 0.5683 | 0.7061 | 0.3760 | 0.6554 | 0.4089 | 0.3209 | 0.4508 | 0.8982 | 0.2658 | 0.3982 | 0.6053 | 0.5668 | 0.5037 | +| 0.7509 | 29500 | 3.4826 | 1.8855 | 8.9320 | 7.5047 | 8.2470 | 12.5056 | 3.1168 | 7.8967 | 2.5742 | 8.3762 | 20.4917 | 1.7122 | 6.3650 | 0.9882 | 0.3380 | 0.5681 | 0.7061 | 0.3727 | 0.6521 | 0.4071 | 0.3222 | 0.4771 | 0.8963 | 0.2701 | 0.3943 | 0.6010 | 0.5624 | 0.5052 | +| 0.7572 | 29750 | 3.4268 | 1.8725 | 8.9332 | 7.5158 | 8.2481 | 12.4708 | 3.1146 | 7.9010 | 2.5617 | 8.2478 | 20.3435 | 1.6860 | 6.4246 | 0.9801 | 0.3331 | 0.5691 | 0.7061 | 0.3767 | 0.6523 | 0.4068 | 0.3195 | 0.4589 | 0.9097 | 0.2627 | 0.4113 | 0.6059 | 0.5641 | 0.5059 | +| 0.7636 | 30000 | 3.2621 | 1.8813 | 8.9125 | 7.5203 | 8.2736 | 12.5555 | 3.1094 | 7.9083 | 2.5488 | 8.3690 | 20.3392 | 1.7015 | 6.5219 | 0.9901 | 0.3366 | 0.5686 | 0.6941 | 0.3696 | 0.6465 | 0.4062 | 0.3228 | 0.4570 | 0.8967 | 0.2657 | 0.3975 | 0.6054 | 0.5702 | 0.5028 | +| 0.7700 | 30250 | 3.3289 | 1.8893 | 8.9211 | 7.5322 | 8.1753 | 12.4890 | 3.1063 | 7.9272 | 2.5320 | 8.4628 | 20.3169 | 1.6841 | 6.5986 | 0.9798 | 0.3302 | 0.5719 | 0.6944 | 0.3745 | 0.6471 | 0.4007 | 0.3172 | 0.4741 | 0.9046 | 0.2685 | 0.4126 | 0.6014 | 0.5666 | 0.5049 | +| 0.7763 | 30500 | 3.5363 | 1.8836 | 8.8909 | 7.5323 | 8.2197 | 12.4000 | 3.1064 | 7.9088 | 2.5494 | 8.3271 | 20.3110 | 1.7132 | 6.4502 | 0.9974 | 0.3298 | 0.5705 | 0.6995 | 0.3671 | 0.6511 | 0.4057 | 0.3204 | 0.4560 | 0.8949 | 0.2613 | 0.4153 | 0.6011 | 0.5738 | 0.5036 | +| 0.7827 | 30750 | 3.3557 | 1.8824 | 8.9002 | 7.5249 | 8.2047 | 12.4618 | 3.1055 | 7.9265 | 2.5501 | 8.2708 | 20.2254 | 1.7222 | 6.4927 | 0.9813 | 0.3333 | 0.5678 | 0.6926 | 0.3781 | 0.6575 | 0.4005 | 0.3225 | 0.4497 | 0.8991 | 0.2649 | 0.4018 | 0.6051 | 0.5698 | 0.5033 | +| 0.7891 | 31000 | 3.4481 | 1.8725 | 8.9077 | 7.5043 | 8.2095 | 12.5197 | 3.1095 | 7.9124 | 2.5216 | 8.1396 | 20.0618 | 1.6962 | 6.4808 | 0.9764 | 0.3321 | 0.5691 | 0.6941 | 0.3650 | 0.6464 | 0.4013 | 0.3239 | 0.4522 | 0.9035 | 0.2657 | 0.4058 | 0.6041 | 0.5639 | 0.5021 | +| 0.7954 | 31250 | 3.3888 | 1.8596 | 8.9249 | 7.5248 | 8.2429 | 12.4622 | 3.1054 | 7.9247 | 2.5351 | 8.3011 | 19.9880 | 1.7062 | 6.5598 | 0.9628 | 0.3428 | 0.5694 | 0.7008 | 0.3761 | 0.6564 | 0.4013 | 0.3223 | 0.4589 | 0.9024 | 0.2633 | 0.4111 | 0.6052 | 0.5670 | 0.5059 | +| 0.8018 | 31500 | 3.3989 | 1.8662 | 8.9299 | 7.5206 | 8.1957 | 12.4667 | 3.1043 | 7.9089 | 2.5465 | 8.2149 | 19.8897 | 1.6830 | 6.6131 | 0.9204 | 0.3452 | 0.5658 | 0.7002 | 0.3709 | 0.6570 | 0.4004 | 0.3233 | 0.4588 | 0.9015 | 0.2619 | 0.4044 | 0.6015 | 0.5654 | 0.5043 | +| 0.8082 | 31750 | 3.3603 | 1.8671 | 8.9158 | 7.5135 | 8.1945 | 12.5201 | 3.1009 | 7.9130 | 2.5539 | 8.3876 | 19.9818 | 1.6685 | 6.6359 | 0.9307 | 0.3516 | 0.5659 | 0.6928 | 0.3767 | 0.6534 | 0.4004 | 0.3245 | 0.4627 | 0.9021 | 0.2619 | 0.4252 | 0.6019 | 0.5699 | 0.5068 | +| 0.8145 | 32000 | 3.3783 | 1.8588 | 8.8993 | 7.4994 | 8.2058 | 12.5200 | 3.0980 | 7.8902 | 2.5448 | 8.3444 | 19.9960 | 1.6815 | 6.7156 | 0.9509 | 0.3358 | 0.5665 | 0.7012 | 0.3759 | 0.6551 | 0.4002 | 0.3259 | 0.4505 | 0.8960 | 0.2621 | 0.4113 | 0.6059 | 0.5716 | 0.5045 | +| 0.8209 | 32250 | 3.4379 | 1.8693 | 8.8896 | 7.4639 | 8.0939 | 12.5840 | 3.0894 | 7.8559 | 2.5590 | 8.4227 | 19.9565 | 1.6855 | 6.6835 | 0.9650 | 0.3433 | 0.5663 | 0.7004 | 0.3717 | 0.6527 | 0.4076 | 0.3255 | 0.4491 | 0.8990 | 0.2643 | 0.4127 | 0.5984 | 0.5741 | 0.5050 | +| 0.8272 | 32500 | 3.4962 | 1.8838 | 8.8634 | 7.4636 | 8.1354 | 12.5144 | 3.0866 | 7.8561 | 2.5410 | 8.4169 | 20.0794 | 1.6625 | 6.6897 | 0.9849 | 0.3461 | 0.5678 | 0.7024 | 0.3808 | 0.6564 | 0.4083 | 0.3200 | 0.4531 | 0.9015 | 0.2622 | 0.4116 | 0.6061 | 0.5685 | 0.5065 | +| 0.8336 | 32750 | 3.4927 | 1.8696 | 8.8655 | 7.4274 | 8.0888 | 12.5431 | 3.0874 | 7.8320 | 2.5324 | 8.3181 | 20.0095 | 1.6403 | 6.7182 | 0.9837 | 0.3439 | 0.5657 | 0.7078 | 0.3771 | 0.6580 | 0.4078 | 0.3210 | 0.4543 | 0.9087 | 0.2625 | 0.4184 | 0.6095 | 0.5691 | 0.5080 | +| 0.8400 | 33000 | 3.33 | 1.8743 | 8.8776 | 7.4438 | 8.1127 | 12.5571 | 3.0793 | 7.8474 | 2.5423 | 8.3195 | 20.1064 | 1.6546 | 6.7761 | 1.0118 | 0.3400 | 0.5684 | 0.6938 | 0.3760 | 0.6560 | 0.4077 | 0.3248 | 0.4586 | 0.8965 | 0.2613 | 0.4085 | 0.6094 | 0.5704 | 0.5055 | +| 0.8463 | 33250 | 3.3431 | 1.8687 | 8.8074 | 7.4577 | 8.1313 | 12.5392 | 3.0816 | 7.8629 | 2.5306 | 8.3183 | 20.1216 | 1.6653 | 6.8390 | 1.0199 | 0.3233 | 0.5685 | 0.7031 | 0.3770 | 0.6493 | 0.4075 | 0.3243 | 0.4586 | 0.9015 | 0.2600 | 0.4068 | 0.6050 | 0.5659 | 0.5039 | +| 0.8527 | 33500 | 3.4455 | 1.8618 | 8.8108 | 7.4587 | 8.1421 | 12.5672 | 3.0800 | 7.8533 | 2.5364 | 8.2589 | 20.0766 | 1.6537 | 6.8825 | 1.0161 | 0.3346 | 0.5647 | 0.7003 | 0.3761 | 0.6523 | 0.4075 | 0.3239 | 0.4598 | 0.9088 | 0.2641 | 0.4134 | 0.5976 | 0.5702 | 0.5056 | +| 0.8591 | 33750 | 3.3189 | 1.8578 | 8.8046 | 7.4684 | 8.1488 | 12.6076 | 3.0817 | 7.8637 | 2.5249 | 8.2008 | 20.1733 | 1.6486 | 6.9228 | 1.0004 | 0.3428 | 0.5684 | 0.7019 | 0.3782 | 0.6483 | 0.4049 | 0.3229 | 0.4525 | 0.9039 | 0.2641 | 0.4088 | 0.6114 | 0.5669 | 0.5058 | +| 0.8654 | 34000 | 3.3815 | 1.8587 | 8.7921 | 7.4675 | 8.0968 | 12.6359 | 3.0800 | 7.8680 | 2.5266 | 8.3492 | 20.1037 | 1.6352 | 6.8485 | 1.0061 | 0.3412 | 0.5655 | 0.6945 | 0.3806 | 0.6510 | 0.4061 | 0.3178 | 0.4583 | 0.9085 | 0.2640 | 0.4078 | 0.6105 | 0.5689 | 0.5058 | +| 0.8718 | 34250 | 3.3381 | 1.8586 | 8.7828 | 7.4672 | 8.1115 | 12.6341 | 3.0783 | 7.8743 | 2.5275 | 8.3363 | 20.0616 | 1.6445 | 6.8898 | 1.0146 | 0.3255 | 0.5645 | 0.6941 | 0.3784 | 0.6488 | 0.4027 | 0.3190 | 0.4518 | 0.9039 | 0.2637 | 0.4079 | 0.6115 | 0.5730 | 0.5034 | +| 0.8782 | 34500 | 3.3992 | 1.8597 | 8.7906 | 7.4658 | 8.1316 | 12.6647 | 3.0781 | 7.8643 | 2.5249 | 8.3280 | 20.0237 | 1.6348 | 6.8277 | 1.0203 | 0.3283 | 0.5645 | 0.6925 | 0.3744 | 0.6525 | 0.4027 | 0.3250 | 0.4593 | 0.9040 | 0.2625 | 0.4097 | 0.6121 | 0.5676 | 0.5042 | +| 0.8845 | 34750 | 3.2951 | 1.8608 | 8.8033 | 7.4729 | 8.1063 | 12.6896 | 3.0719 | 7.8671 | 2.5243 | 8.3368 | 20.0587 | 1.6455 | 6.7907 | 1.0106 | 0.3274 | 0.5651 | 0.6925 | 0.3738 | 0.6527 | 0.4045 | 0.3219 | 0.4592 | 0.9041 | 0.2619 | 0.3985 | 0.6120 | 0.5679 | 0.5032 | +| 0.8909 | 35000 | 3.262 | 1.8677 | 8.8024 | 7.4317 | 8.1170 | 12.7225 | 3.0671 | 7.8519 | 2.5303 | 8.4655 | 20.1156 | 1.6405 | 6.7997 | 1.0141 | 0.3288 | 0.5667 | 0.6935 | 0.3738 | 0.6554 | 0.4049 | 0.3196 | 0.4532 | 0.9041 | 0.2626 | 0.4116 | 0.6123 | 0.5653 | 0.5040 | +| 0.8972 | 35250 | 3.3218 | 1.8698 | 8.7992 | 7.4495 | 8.1390 | 12.7198 | 3.0627 | 7.8528 | 2.5399 | 8.4549 | 20.1652 | 1.6288 | 6.7857 | 1.0261 | 0.3328 | 0.5666 | 0.6951 | 0.3686 | 0.6538 | 0.4043 | 0.3248 | 0.4596 | 0.9042 | 0.2646 | 0.4097 | 0.6123 | 0.5725 | 0.5053 | +| 0.9036 | 35500 | 3.3529 | 1.8633 | 8.7964 | 7.4339 | 8.1347 | 12.7295 | 3.0657 | 7.8262 | 2.5256 | 8.4119 | 20.1363 | 1.6165 | 6.7987 | 1.0260 | 0.3268 | 0.5660 | 0.6951 | 0.3695 | 0.6543 | 0.4042 | 0.3232 | 0.4511 | 0.9068 | 0.2666 | 0.4047 | 0.6112 | 0.5701 | 0.5038 | +| 0.9100 | 35750 | 3.2205 | 1.8626 | 8.7751 | 7.4335 | 8.1346 | 12.7532 | 3.0654 | 7.8318 | 2.5077 | 8.3883 | 20.0738 | 1.5839 | 6.7819 | 1.0282 | 0.3291 | 0.5659 | 0.6951 | 0.3702 | 0.6530 | 0.4045 | 0.3241 | 0.4493 | 0.9067 | 0.2622 | 0.4192 | 0.6112 | 0.5710 | 0.5047 | +| 0.9163 | 36000 | 3.3671 | 1.8521 | 8.7693 | 7.4500 | 8.1155 | 12.8041 | 3.0671 | 7.8451 | 2.4994 | 8.3053 | 20.0666 | 1.5984 | 6.7696 | 1.0377 | 0.3383 | 0.5667 | 0.6946 | 0.3739 | 0.6536 | 0.4049 | 0.3262 | 0.4457 | 0.9065 | 0.2625 | 0.4143 | 0.6112 | 0.5719 | 0.5054 | +| 0.9227 | 36250 | 3.4074 | 1.8555 | 8.7683 | 7.4607 | 8.1465 | 12.7545 | 3.0621 | 7.8488 | 2.5080 | 8.3816 | 20.0420 | 1.6043 | 6.7991 | 1.0539 | 0.3367 | 0.5685 | 0.6935 | 0.3714 | 0.6523 | 0.4068 | 0.3237 | 0.4471 | 0.9065 | 0.2645 | 0.4176 | 0.6110 | 0.5692 | 0.5053 | +| 0.9291 | 36500 | 3.4806 | 1.8543 | 8.7833 | 7.4584 | 8.1070 | 12.7444 | 3.0605 | 7.8475 | 2.5166 | 8.3809 | 19.3340 | 1.6181 | 6.7663 | 1.0555 | 0.3271 | 0.5693 | 0.6935 | 0.3733 | 0.6560 | 0.3990 | 0.3229 | 0.4530 | 0.9065 | 0.2618 | 0.4105 | 0.6129 | 0.5654 | 0.5039 | +| 0.9354 | 36750 | 3.4848 | 1.8558 | 8.7967 | 7.4512 | 8.1175 | 12.6960 | 3.0616 | 7.8420 | 2.5137 | 8.4267 | 19.4008 | 1.6181 | 6.7376 | 1.0629 | 0.3284 | 0.5697 | 0.6922 | 0.3723 | 0.6530 | 0.4048 | 0.3234 | 0.4534 | 0.9065 | 0.2641 | 0.4088 | 0.6115 | 0.5635 | 0.5040 | +| 0.9418 | 37000 | 3.1947 | 1.8574 | 8.8026 | 7.4555 | 8.1222 | 12.7155 | 3.0591 | 7.8391 | 2.5180 | 8.4551 | 19.4094 | 1.6134 | 6.7200 | 1.0657 | 0.3318 | 0.5681 | 0.6922 | 0.3711 | 0.6550 | 0.4043 | 0.3234 | 0.4534 | 0.9037 | 0.2645 | 0.4090 | 0.6113 | 0.5685 | 0.5043 | +| 0.9482 | 37250 | 3.3557 | 1.8569 | 8.7972 | 7.4535 | 8.1200 | 12.7427 | 3.0585 | 7.8460 | 2.5167 | 8.5049 | 19.4395 | 1.6141 | 6.7080 | 1.0700 | 0.3308 | 0.5699 | 0.6930 | 0.3708 | 0.6513 | 0.4048 | 0.3247 | 0.4589 | 0.9039 | 0.2645 | 0.4087 | 0.6113 | 0.5677 | 0.5046 | +| 0.9545 | 37500 | 3.369 | 1.8580 | 8.7958 | 7.4558 | 8.1193 | 12.7407 | 3.0579 | 7.8509 | 2.5139 | 8.5231 | 19.4691 | 1.6139 | 6.7228 | 1.0696 | 0.3388 | 0.5683 | 0.6922 | 0.3696 | 0.6556 | 0.4050 | 0.3236 | 0.4460 | 0.8965 | 0.2624 | 0.4145 | 0.6113 | 0.5682 | 0.5040 | +| 0.9609 | 37750 | 3.398 | 1.8564 | 8.7955 | 7.4567 | 8.1174 | 12.7478 | 3.0589 | 7.8415 | 2.5086 | 8.5030 | 19.4719 | 1.5989 | 6.7117 | 1.0689 | 0.3381 | 0.5678 | 0.6922 | 0.3652 | 0.6544 | 0.4049 | 0.3234 | 0.4460 | 0.9039 | 0.2599 | 0.3998 | 0.6113 | 0.5658 | 0.5025 | +| 0.9672 | 38000 | 3.3699 | 1.8534 | 8.8011 | 7.4604 | 8.1135 | 12.7689 | 3.0574 | 7.8412 | 2.5097 | 8.4866 | 19.4973 | 1.5966 | 6.7309 | 1.0655 | 0.3372 | 0.5680 | 0.6922 | 0.3651 | 0.6553 | 0.4038 | 0.3224 | 0.4560 | 0.9025 | 0.2675 | 0.4061 | 0.6111 | 0.5712 | 0.5045 | +| 0.9736 | 38250 | 3.4483 | 1.8540 | 8.8045 | 7.4506 | 8.0942 | 12.7725 | 3.0569 | 7.8379 | 2.5135 | 8.4817 | 19.5038 | 1.5995 | 6.7289 | 1.0693 | 0.3378 | 0.5682 | 0.6922 | 0.3724 | 0.6550 | 0.4038 | 0.3243 | 0.4534 | 0.9025 | 0.2645 | 0.4058 | 0.6111 | 0.5710 | 0.5048 | +| 0.9800 | 38500 | 3.254 | 1.8546 | 8.8026 | 7.4530 | 8.1004 | 12.7796 | 3.0559 | 7.8378 | 2.5086 | 8.4538 | 19.5061 | 1.5997 | 6.7390 | 1.0724 | 0.3381 | 0.5681 | 0.6922 | 0.3647 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2642 | 0.4064 | 0.6111 | 0.5677 | 0.5034 | +| 0.9863 | 38750 | 3.2759 | 1.8549 | 8.8024 | 7.4545 | 8.0996 | 12.7875 | 3.0562 | 7.8400 | 2.5054 | 8.4401 | 19.5192 | 1.6018 | 6.7433 | 1.0737 | 0.3382 | 0.5681 | 0.6922 | 0.3647 | 0.6547 | 0.4041 | 0.3241 | 0.4534 | 0.8951 | 0.2642 | 0.4078 | 0.6111 | 0.5689 | 0.5036 | +| 0.9927 | 39000 | 3.3273 | 1.8548 | 8.8017 | 7.4536 | 8.0908 | 12.7885 | 3.0557 | 7.8396 | 2.5052 | 8.4335 | 19.5169 | 1.6002 | 6.7439 | 1.0716 | 0.3382 | 0.5681 | 0.6922 | 0.3647 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2641 | 0.4078 | 0.6111 | 0.5703 | 0.5037 | +| 0.9991 | 39250 | 3.3902 | 1.8540 | 8.8010 | 7.4543 | 8.0872 | 12.7890 | 3.0561 | 7.8412 | 2.5040 | 8.3945 | 19.5165 | 1.5997 | 6.7462 | 1.0718 | 0.3309 | 0.5681 | 0.6922 | 0.3651 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2643 | 0.4078 | 0.6111 | 0.5703 | 0.5032 | +| 1.0 | 39287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.3309 | 0.5681 | 0.6922 | 0.3651 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2643 | 0.4078 | 0.6111 | 0.5703 | 0.5032 | + +
+ +### Environmental Impact +Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). +- **Energy Consumed**: 2.611 kWh +- **Carbon Emitted**: 1.015 kg of CO2 +- **Hours Used**: 17.883 hours + +### Training Hardware +- **On Cloud**: No +- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 +- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K +- **RAM Size**: 31.78 GB + +### Framework Versions +- Python: 3.11.6 +- Sentence Transformers: 3.3.0.dev0 +- Transformers: 4.45.2 +- PyTorch: 2.5.0.dev20240807+cu121 +- Accelerate: 1.0.0 +- Datasets: 2.20.0 +- Tokenizers: 0.20.1-dev.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", +} +``` + +#### MatryoshkaLoss +```bibtex +@misc{kusupati2024matryoshka, + title={Matryoshka Representation Learning}, + author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, + year={2024}, + eprint={2205.13147}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + +#### MultipleNegativesRankingLoss +```bibtex +@misc{henderson2017efficient, + title={Efficient Natural Language Response Suggestion for Smart Reply}, + author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, + year={2017}, + eprint={1705.00652}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + + + + + + \ No newline at end of file