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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:98112 |
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- loss:MultipleNegativesRankingLoss |
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base_model: thenlper/gte-small |
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widget: |
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- source_sentence: How does a photocell control outdoor lighting? |
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sentences: |
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- 'To solve this problem, we can use the binomial probability formula: |
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P(X = k) = C(n, k) * p^k * (1-p)^(n-k) |
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where: |
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- P(X = k) is the probability of exactly k successes (faulty keyboards) in n trials |
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(laptops produced) |
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- C(n, k) is the number of combinations of n items taken k at a time (n! / (k!(n-k)!)) |
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- p is the probability of success (5% or 0.05) |
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- n is the number of trials (400 laptops) |
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- k is the number of successes (20 faulty keyboards) |
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However, we want to find the probability of at least 20 faulty keyboards, so we |
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need to find the sum of probabilities for k = 20, 21, 22, ..., 400. |
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P(X >= 20) = 1 - P(X < 20) = 1 - Σ P(X = k) for k = 0 to 19 |
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Now, we can calculate the probabilities for each value of k and sum them up: |
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P(X >= 20) = 1 - Σ C(400, k) * 0.05^k * 0.95^(400-k) for k = 0 to 19 |
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Using a calculator or software to compute the sum, we get: |
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P(X >= 20) ≈ 1 - 0.0184 = 0.9816 |
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So, the probability that at least 20 laptops will have a faulty keyboard is approximately |
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98.16%.' |
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- A photocell controls outdoor lighting by detecting the level of ambient light. |
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It automatically turns the lights on when it becomes dark and off when it becomes |
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light, functioning as a light-dependent switch for energy efficiency and convenience. |
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- 'Glycosylation with β-N-acetylglucosamine (O-GlcNAcylation) is one of the most |
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complex post-translational modifications. The cycling of O-GlcNAc is controlled |
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by two enzymes: UDP-NAc transferase (OGT) and O-GlcNAcase (OGA). We recently reported |
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that endothelin-1 (ET-1) augments vascular levels of O-GlcNAcylated proteins. |
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Here we tested the hypothesis that O-GlcNAcylation contributes to the vascular |
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effects of ET-1 via activation of the RhoA/Rho-kinase pathway. Incubation of vascular |
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smooth muscle cells (VSMCs) with ET-1 (0.1 μM) produces a time-dependent increase |
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in O-GlcNAc levels. ET-1-induced O-GlcNAcylation is not observed when VSMCs are |
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previously transfected with OGT siRNA, treated with ST045849 (OGT inhibitor) or |
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atrasentan (ET(A) antagonist). ET-1 as well as PugNAc (OGA inhibitor) augmented |
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contractions to phenylephrine in endothelium-denuded rat aortas, an effect that |
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was abolished by the Rho kinase inhibitor Y-27632. Incubation of VSMCs with ET-1 |
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increased expression of the phosphorylated forms of myosin phosphatase target |
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subunit 1 (MYPT-1), protein kinase C-potentiated protein phosphatase 1 inhibitor |
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protein (protein kinase C-potentiated phosphatase inhibitor-17), and myosin light |
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chain (MLC) and RhoA expression and activity, and this effect was abolished by |
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both OGT siRNA transfection or OGT inhibition and atrasentan. ET-1 also augmented |
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expression of PDZ-Rho GEF (guanine nucleotide exchange factor) and p115-Rho GEF |
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in VSMCs and this was prevented by OGT siRNA, ST045849, and atrasentan.' |
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- source_sentence: A torus has a major radius of 5 cm and a minor radius of 3 cm. |
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Find the volume of the torus. |
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sentences: |
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- 'To find the Hausdorff dimension of the Koch curve, we can use the formula: |
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Hausdorff dimension (D) = log(N) / log(1/s) |
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where N is the number of self-similar pieces and s is the scaling factor. |
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For the Koch curve, each line segment is divided into four segments, each of which |
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is 1/3 the length of the original segment. Therefore, N = 4 and s = 1/3. |
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Now, we can plug these values into the formula: |
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D = log(4) / log(1/3) |
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D ≈ 1.2619 |
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So, the Hausdorff dimension of the Koch curve is approximately 1.2619.' |
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- 'To find the volume of a torus, we can use the formula: |
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Volume = (π * minor_radius^2) * (2 * π * major_radius) |
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where minor_radius is the minor radius of the torus and major_radius is the major |
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radius of the torus. |
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Given that the major radius is 5 cm and the minor radius is 3 cm, we can plug |
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these values into the formula: |
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Volume = (π * 3^2) * (2 * π * 5) |
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Volume = (π * 9) * (10 * π) |
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Volume = 90 * π^2 |
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The volume of the torus is approximately 282.74 cubic centimeters.' |
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- The purpose of the present study was to elucidate the mechanisms of action mediating |
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enhancement of basal glucose uptake in skeletal muscle cells by seven medicinal |
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plant products recently identified from the pharmacopeia of native Canadian populations |
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(Spoor et al., 2006). Activity of the major signaling pathways that regulate glucose |
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uptake was assessed by western immunoblot in C2C12 muscle cells treated with extracts |
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from these plant species. Effects of extracts on mitochondrial function were assessed |
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by respirometry in isolated rat liver mitochondria. Metabolic stress induced by |
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extracts was assessed by measuring ATP concentration and rate of cell medium acidification |
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in C2C12 myotubes and H4IIE hepatocytes. Extracts were applied at a dose of 15-100 |
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microg/ml. The effect of all seven products was achieved through a common mechanism |
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mediated not by the insulin signaling pathway but rather by the AMP-activated |
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protein kinase (AMPK) pathway in response to the disruption of mitochondrial function |
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and ensuing metabolic stress. Disruption of mitochondrial function occurred in |
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the form of uncoupling of oxidative phosphorylation and/or inhibition of ATPsynthase. |
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Activity of the AMPK pathway, in some instances comparable to that stimulated |
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by 4mM of the AMP-mimetic AICAR, was in several cases sustained for at least 18h |
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post-treatment. Duration of metabolic stress, however, was in most cases in the |
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order of 1h. |
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- source_sentence: Consider the elliptic curve given by the equation $y^2=x^3-2x+5$ |
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over the field of rational numbers $\mathbb{Q}$. Let $P=(1,2)$ and $Q=(-1,2)$ |
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be two points on the curve. Find the equation of the line passing through $P$ |
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and $Q$ and show that it intersects the curve at another point $R$. Then, find |
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the coordinates of the point $R$. |
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sentences: |
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- Fifteen novel derivatives of D-DIBOA, including aromatic ring modifications and |
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the addition of side chains in positions C-2 and N-4, had previously been synthesised |
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and their phytotoxicity on standard target species (STS) evaluated. This strategy |
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combined steric, electronic, solubility and lipophilicity requirements to achieve |
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the maximum phytotoxic activity. An evaluation of the bioactivity of these compounds |
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on the systems Oryza sativa-Echinochloa crus-galli and Triticum aestivum-Avena |
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fatua is reported here. All compounds showed inhibition profiles on the two species |
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Echinochloa crus-galli (L.) Beauv. and Avena fatua L. The most marked effects |
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were caused by 6F-4Pr-D-DIBOA, 6F-4Val-D-DIBOA, 6Cl-4Pr-D-DIBOA and 6Cl-4Val-D-DIBOA. |
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The IC(50) values for the systems Echinochloa crus-galli-Oryza sativa and Avena |
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fatua-Triticum aestivum for all compounds were compared. The compound that showed |
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the greatest selectivity for the system Echinochloa crus-galli-Oryza sativa was |
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8Cl-4Pr-D-DIBOA, which was 15 times more selective than the commercial herbicide |
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propanil (Cotanil-35). With regard to the system Avena fatua-Triticum aestivum, |
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the compounds that showed the highest selectivities were 8Cl-4Val-D-DIBOA and |
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6F-4Pr-D-DIBOA. The results obtained for 6F-4Pr-D-DIBOA are of great interest |
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because of the high phytotoxicity to Avena fatua (IC(50) = 6 µM, r(2) = 0.9616). |
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- 'To find the equation of the line passing through points $P=(1,2)$ and $Q=(-1,2)$, |
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we first find the slope of the line. Since the y-coordinates of both points are |
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the same, the slope is 0. Therefore, the line is horizontal and its equation is |
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given by: |
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$y = 2$ |
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Now, we want to find the point $R$ where this line intersects the elliptic curve |
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$y^2 = x^3 - 2x + 5$. Since we know that $y=2$, we can substitute this value into |
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the equation of the curve: |
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$(2)^2 = x^3 - 2x + 5$ |
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Simplifying, we get: |
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$4 = x^3 - 2x + 5$ |
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Rearranging the terms, we have: |
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$x^3 - 2x + 1 = 0$ |
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We know that $x=1$ and $x=-1$ are solutions to this equation since they correspond |
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to the points $P$ and $Q$. To find the third solution, we can use synthetic division |
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or factor the polynomial. Factoring, we get: |
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$(x-1)(x+1)(x-1) = 0$ |
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So, the third solution is $x=1$. Substituting this value back into the equation |
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of the line, we find the corresponding y-coordinate: |
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$y = 2$ |
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Thus, the third point of intersection is $R=(1,2)$. However, in the context of |
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elliptic curves, we should take the "sum" of the points $P$ and $Q$ as the negative |
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of the third intersection point. Since $R=(1,2)$, the negative of this point is |
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given by $-R=(1,-2)$. Therefore, the "sum" of the points $P$ and $Q$ on the elliptic |
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curve is: |
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$P + Q = -R = (1,-2)$.' |
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- The use of geospatial analysis may be subject to regulatory compliance depending |
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on the specific application and the jurisdiction in which it is used. For example, |
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the use of geospatial data for marketing purposes may be subject to privacy regulations, |
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and the use of geospatial data for land use planning may be subject to environmental |
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regulations. It is important to consult with legal counsel to ensure compliance |
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with all applicable laws and regulations. |
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- source_sentence: Does sLEDAI-2K Conceal Worsening in a Particular System When There |
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Is Overall Improvement? |
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sentences: |
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- To determine whether the Systemic Lupus Erythematosus Disease Activity Index 2000 |
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(SLEDAI-2K) is valid in identifying patients who had a clinically important overall |
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improvement with no worsening in other descriptors/systems. Consecutive patients |
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with systemic lupus erythematosus with active disease who attended the Lupus Clinic |
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between 2000 and 2012 were studied. Based on the change in the total SLEDAI-2K |
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scores on last visit, patients were grouped as improved, flared/worsened, and |
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unchanged. Patients showing improvement were evaluated for the presence of new |
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active descriptors at last visit compared with baseline visit. Of the 158 patients |
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studied, 109 patients had improved, 38 remained unchanged, and 11 flared/worsened |
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at last visit. In the improved group, 11 patients had a new laboratory descriptor |
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that was not present at baseline visit. In those 11 patients, this new laboratory |
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descriptor was not clinically significant and did not require a change in disease |
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management. |
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- 'To find the dot product of two vectors using their magnitudes, angle between |
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them, and trigonometry, we can use the formula: |
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Dot product = |A| * |B| * cos(θ) |
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where |A| and |B| are the magnitudes of the vectors, and θ is the angle between |
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them. |
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In this case, |A| = 5 units, |B| = 8 units, and θ = 60 degrees. |
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First, we need to convert the angle from degrees to radians: |
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θ = 60 * (π / 180) = π / 3 radians |
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Now, we can find the dot product: |
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Dot product = 5 * 8 * cos(π / 3) |
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Dot product = 40 * (1/2) |
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Dot product = 20 |
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So, the dot product of the two vectors is 20.' |
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- To determine if hospitals that routinely discharge patients early after lobectomy |
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have increased readmissions. Hospitals are increasingly motivated to reduce length |
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of stay (LOS) after lung cancer surgery, yet it is unclear if a routine of early |
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discharge is associated with increased readmissions. The relationship between |
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hospital discharge practices and readmission rates is therefore of tremendous |
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clinical and financial importance. The National Cancer Database was queried for |
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patients undergoing lobectomy for lung cancer from 2004 to 2013 at Commission |
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on Cancer-accredited hospitals, which performed at least 25 lobectomies in a 2-year |
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period. Facility discharge practices were characterized by a facility's median |
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LOS relative to the median LOS for all patients in that same time period. In all, |
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59,734 patients met inclusion criteria; 2687 (4.5%) experienced an unplanned readmission. |
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In a hierarchical logistic regression model, a routine of early discharge (defined |
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as a facility's tendency to discharge patients faster than the population median |
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in the same time period) was not associated with increased risk of readmission |
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(odds ratio 1.12, 95% confidence interval 0.97-1.28, P = 0.12). In a risk-adjusted |
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hospital readmission rate analysis, hospitals that discharged patients early did |
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not experience more readmissions (P = 0.39). The lack of effect of early discharge |
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practices on readmission rates was observed for both minimally invasive and thoracotomy |
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approaches. |
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- source_sentence: Does systemic administration of urocortin after intracerebral hemorrhage |
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reduce neurological deficits and neuroinflammation in rats? |
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sentences: |
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- Intracerebral hemorrhage (ICH) remains a serious clinical problem lacking effective |
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treatment. Urocortin (UCN), a novel anti-inflammatory neuropeptide, protects injured |
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cardiomyocytes and dopaminergic neurons. Our preliminary studies indicate UCN |
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alleviates ICH-induced brain injury when administered intracerebroventricularly |
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(ICV). The present study examines the therapeutic effect of UCN on ICH-induced |
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neurological deficits and neuroinflammation when administered by the more convenient |
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intraperitoneal (i.p.) route. ICH was induced in male Sprague-Dawley rats by intrastriatal |
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infusion of bacterial collagenase VII-S or autologous blood. UCN (2.5 or 25 μg/kg) |
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was administered i.p. at 60 minutes post-ICH. Penetration of i.p. administered |
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fluorescently labeled UCN into the striatum was examined by fluorescence microscopy. |
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Neurological deficits were evaluated by modified neurological severity score (mNSS). |
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Brain edema was assessed using the dry/wet method. Blood-brain barrier (BBB) disruption |
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was assessed using the Evans blue assay. Hemorrhagic volume and lesion volume |
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were assessed by Drabkin's method and morphometric assay, respectively. Pro-inflammatory |
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cytokine (TNF-α, IL-1β, and IL-6) expression was evaluated by enzyme-linked immunosorbent |
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assay (ELISA). Microglial activation and neuronal loss were evaluated by immunohistochemistry. |
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Administration of UCN reduced neurological deficits from 1 to 7 days post-ICH. |
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Surprisingly, although a higher dose (25 μg/kg, i.p.) also reduced the functional |
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deficits associated with ICH, it is significantly less effective than the lower |
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dose (2.5 μg/kg, i.p.). Beneficial results with the low dose of UCN included a |
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reduction in neurological deficits from 1 to 7 days post-ICH, as well as a reduction |
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in brain edema, BBB disruption, lesion volume, microglial activation and neuronal |
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loss 3 days post-ICH, and suppression of TNF-α, IL-1β, and IL-6 production 1, |
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3 and 7 days post-ICH. |
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- 'A perfect number is a positive integer that is equal to the sum of its proper |
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divisors (excluding itself). The first perfect numbers are 6, 28, 496, and 8128. |
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Perfect numbers can be generated using the formula 2^(p-1) * (2^p - 1), where |
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p and 2^p - 1 are both prime numbers. |
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The first five (p, 2^p - 1) pairs are: |
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(2, 3) - 6 |
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(3, 7) - 28 |
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(5, 31) - 496 |
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(7, 127) - 8128 |
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(13, 8191) - 33,550,336 |
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To find the 6th perfect number, we need to find the next prime number p such that |
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2^p - 1 is also prime. The next such pair is (17, 131071). Using the formula: |
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2^(17-1) * (2^17 - 1) = 2^16 * 131071 = 65,536 * 131071 = 8,589,869,056 |
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So, the 6th perfect number is 8,589,869,056.' |
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- 'In type theory, the successor function $S$ is used to represent the next number |
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in the sequence. When you apply the successor function $S$ three times to the |
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number 0, you get: |
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1. $S(0)$, which represents 1. |
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2. $S(S(0))$, which represents 2. |
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3. $S(S(S(0)))$, which represents 3. |
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So, the result of applying the successor function $S$ three times to the number |
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0 in type theory is 3.' |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: SentenceTransformer based on thenlper/gte-small |
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results: |
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- task: |
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type: logging |
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name: Logging |
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dataset: |
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name: ir eval |
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type: ir-eval |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.9291020819957809 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.9819315784646427 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9933963129413923 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9984407961111621 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.9291020819957809 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.32731052615488093 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.19867926258827848 |
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name: Cosine Precision@5 |
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- type: cosine_recall@1 |
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value: 0.9291020819957809 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.9819315784646427 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9933963129413923 |
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name: Cosine Recall@5 |
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- type: cosine_ndcg@10 |
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value: 0.9670096227619588 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.9565327512887825 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.9565967419425125 |
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name: Cosine Map@100 |
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--- |
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# SentenceTransformer based on thenlper/gte-small |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sucharush/gte_MNR") |
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# Run inference |
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sentences = [ |
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'Does systemic administration of urocortin after intracerebral hemorrhage reduce neurological deficits and neuroinflammation in rats?', |
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"Intracerebral hemorrhage (ICH) remains a serious clinical problem lacking effective treatment. Urocortin (UCN), a novel anti-inflammatory neuropeptide, protects injured cardiomyocytes and dopaminergic neurons. Our preliminary studies indicate UCN alleviates ICH-induced brain injury when administered intracerebroventricularly (ICV). The present study examines the therapeutic effect of UCN on ICH-induced neurological deficits and neuroinflammation when administered by the more convenient intraperitoneal (i.p.) route. ICH was induced in male Sprague-Dawley rats by intrastriatal infusion of bacterial collagenase VII-S or autologous blood. UCN (2.5 or 25 μg/kg) was administered i.p. at 60 minutes post-ICH. Penetration of i.p. administered fluorescently labeled UCN into the striatum was examined by fluorescence microscopy. Neurological deficits were evaluated by modified neurological severity score (mNSS). Brain edema was assessed using the dry/wet method. Blood-brain barrier (BBB) disruption was assessed using the Evans blue assay. Hemorrhagic volume and lesion volume were assessed by Drabkin's method and morphometric assay, respectively. Pro-inflammatory cytokine (TNF-α, IL-1β, and IL-6) expression was evaluated by enzyme-linked immunosorbent assay (ELISA). Microglial activation and neuronal loss were evaluated by immunohistochemistry. Administration of UCN reduced neurological deficits from 1 to 7 days post-ICH. Surprisingly, although a higher dose (25 μg/kg, i.p.) also reduced the functional deficits associated with ICH, it is significantly less effective than the lower dose (2.5 μg/kg, i.p.). Beneficial results with the low dose of UCN included a reduction in neurological deficits from 1 to 7 days post-ICH, as well as a reduction in brain edema, BBB disruption, lesion volume, microglial activation and neuronal loss 3 days post-ICH, and suppression of TNF-α, IL-1β, and IL-6 production 1, 3 and 7 days post-ICH.", |
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'In type theory, the successor function $S$ is used to represent the next number in the sequence. When you apply the successor function $S$ three times to the number 0, you get:\n\n1. $S(0)$, which represents 1.\n2. $S(S(0))$, which represents 2.\n3. $S(S(S(0)))$, which represents 3.\n\nSo, the result of applying the successor function $S$ three times to the number 0 in type theory is 3.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Logging |
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* Dataset: `ir-eval` |
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* Evaluated with <code>__main__.LoggingEvaluator</code> |
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| Metric | Value | |
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|:-------------------|:----------| |
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| cosine_accuracy@1 | 0.9291 | |
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| cosine_accuracy@3 | 0.9819 | |
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| cosine_accuracy@5 | 0.9934 | |
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| cosine_accuracy@10 | 0.9984 | |
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| cosine_precision@1 | 0.9291 | |
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| cosine_precision@3 | 0.3273 | |
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| cosine_precision@5 | 0.1987 | |
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| cosine_recall@1 | 0.9291 | |
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| cosine_recall@3 | 0.9819 | |
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| cosine_recall@5 | 0.9934 | |
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| **cosine_ndcg@10** | **0.967** | |
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| cosine_mrr@10 | 0.9565 | |
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| cosine_map@100 | 0.9566 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 98,112 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 44.14 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 321.5 tokens</li><li>max: 512 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Are transcobalamin II receptor polymorphisms associated with increased risk for neural tube defects?</code> | <code>Women who have low cobalamin (vitamin B(12)) levels are at increased risk for having children with neural tube defects (NTDs). The transcobalamin II receptor (TCblR) mediates uptake of cobalamin into cells. Inherited variants in the TCblR gene as NTD risk factors were evaluated. Case-control and family-based tests of association were used to screen common variation in TCblR as genetic risk factors for NTDs in a large Irish group. A confirmatory group of NTD triads was used to test positive findings. 2 tightly linked variants associated with NTDs in a recessive model were found: TCblR rs2336573 (G220R; p(corr)=0.0080, corrected for multiple hypothesis testing) and TCblR rs9426 (p(corr)=0.0279). These variants were also associated with NTDs in a family-based test before multiple test correction (log-linear analysis of a recessive model: rs2336573 (G220R; RR=6.59, p=0.0037) and rs9426 (RR=6.71, p=0.0035)). A copy number variant distal to TCblR and two previously unreported exonic insertio...</code> | |
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| <code>A company produces three products: Product A, B, and C. The monthly sales figures and marketing expenses (in thousands of dollars) for each product for the last six months are given below:<br><br>| Product | Sales1 | Sales2 | Sales3 | Sales4 | Sales5 | Sales6 | Marketing Expense1 | Marketing Expense2 | Marketing Expense3 | Marketing Expense4 | Marketing Expense5 | Marketing Expense6 |<br>|---------|--------|--------|--------|--------|--------|--------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|<br>| A | 50 | 45 | 55 | 52 | 48 | 56 | 20 | 18 | 25 | 22 | 19 | 30 |<br>| B | 40 | 48 | 35 | 37 | 45 | 38 | 12 | 15 | 10 | 14 | 17 | 11 |<br>| C | 60 | 65 | ...</code> | <code>To calculate the covariance between the sales of Product A and Product B, we first need to find the mean sales for both products. Then, we will calculate the deviations from the mean for each month's sales and multiply these deviations for both products. Finally, we will sum these products and divide by the number of months minus 1.<br><br>Mean sales for Product A:<br>(50 + 45 + 55 + 52 + 48 + 56) / 6 = 306 / 6 = 51<br><br>Mean sales for Product B:<br>(40 + 48 + 35 + 37 + 45 + 38) / 6 = 243 / 6 = 40.5<br><br>Deviations from the mean for Product A:<br>(50 - 51, 45 - 51, 55 - 51, 52 - 51, 48 - 51, 56 - 51) = (-1, -6, 4, 1, -3, 5)<br><br>Deviations from the mean for Product B:<br>(40 - 40.5, 48 - 40.5, 35 - 40.5, 37 - 40.5, 45 - 40.5, 38 - 40.5) = (-0.5, 7.5, -5.5, -3.5, 4.5, -2.5)<br><br>Multiply the deviations for both products:<br>(-1 * -0.5, -6 * 7.5, 4 * -5.5, 1 * -3.5, -3 * 4.5, 5 * -2.5) = (0.5, 45, -22, -3.5, -13.5, -12.5)<br><br>Sum of the products of deviations:<br>0.5 + 45 - 22 - 3.5 - 13.5 - 12.5 = -6<br><br>Covariance between the sale...</code> | |
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| <code>Consider a basketball player who has a free-throw shooting percentage of 80%. The player attempts 10 free throws in a game. <br><br>If the player makes a free throw, there is an 80% chance that they will make their next free throw attempt. If they miss a free throw, there's a 60% chance that they will make their next free throw attempt.<br><br>What is the probability that the player makes exactly 7 out of their 10 free throw attempts?</code> | <code>To solve this problem, we can use the concept of conditional probability and the binomial theorem. Let's denote the probability of making a free throw after a successful attempt as P(S) = 0.8 and the probability of making a free throw after a missed attempt as P(M) = 0.6.<br><br>We need to find the probability of making exactly 7 out of 10 free throw attempts. There are multiple ways this can happen, and we need to consider all possible sequences of 7 successes (S) and 3 misses (M). We can represent these sequences as a string of S and M, for example, SSSSSSSMMM.<br><br>There are C(10, 7) = 10! / (7! * 3!) = 120 ways to arrange 7 successes and 3 misses in a sequence of 10 attempts. For each of these sequences, we can calculate the probability of that specific sequence occurring and then sum up the probabilities of all sequences.<br><br>Let's calculate the probability of a specific sequence. For example, consider the sequence SSSSSSSMMM. The probability of this sequence occurring is:<br><br>P(SSSSSSSMMM) = P(S...</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 1 |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `tp_size`: 0 |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | ir-eval_cosine_ndcg@10 | |
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|:------:|:----:|:-------------:|:----------------------:| |
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| 0.1631 | 500 | 0.0634 | 0.9563 | |
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| 0.3262 | 1000 | 0.005 | 0.9627 | |
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| 0.4892 | 1500 | 0.0037 | 0.9631 | |
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| 0.6523 | 2000 | 0.0029 | 0.9660 | |
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| 0.8154 | 2500 | 0.0033 | 0.9663 | |
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| 0.9785 | 3000 | 0.0027 | 0.9670 | |
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| 1.0 | 3066 | - | 0.9670 | |
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### Framework Versions |
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- Python: 3.12.8 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.51.3 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.3.0 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
|
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``` |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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