Model: bbmb/deep-learning-for-embedding-model-ssilwal-qpham6
Author: bbmb
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:3078, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: base_model: BAAI/bge-base-en-v1.5
language: en
library_name: sentence-transformers
license: apache-2.0
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:3078, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: [ q_{\text{ut}} = \frac{1}{2} \rho g B N_{\gamma} + c N_{c} + (p_{q} + \rho g D_{f}) N_{q} \quad \text{[SI]} \quad (36.1a) ] [ q_{\text{ut}} = \frac{1}{2} \gamma B N_{\gamma} + c N_{c} + (p_{q} + \gamma D_{f}) N_{q} \quad \text{[U.S.]} \quad (36.1b) ]

Various researchers have made improvements on the theory supporting this equation, leading to somewhat different terms and sophistication in evaluating (N_0), (N_c), and (N_g). The approaches differ in the assumptions made of the shape of the failure zone beneath the footing. However, the general form of the equation is the same in most cases.

Figure 36.2 and Table 36.2 can be used to evaluate the capacity factors (N_0), (N_c), and (N_g) in Equation 36.1. Alternatively, Table 36.3 can be used. The bearing capacity factors in Table 36.2 are based on Terzaghi's 1943 studies. The values in Table 36.3 are based on Meyerhof's 1955 studies and others, and have been widely used. Other values are also in use.

Equation 36.1 is appropriate for a foundation in a continuous wall footing. Corrections, called shape factors, for various footing geometries are presented in Table 36.4 and Table 36.5 using the parameters identified in Figure 36.3. The bearing capacity factors (N_c) and (N_0) are multiplied by the appropriate shape factors when they are used in Equation 36.1.

Several researchers have recommended corrections to (N_0) to account for footing depth. (Corrections to (N_0) for footing depth have also been suggested. No corrections to (N_c) for footing depth have been suggested.) There is considerable variation in the method of calculating this correction if it is used at all. A multiplicative correction factor, (d_c), which is used most often, has the form:

[ d_{c} = 1 + \frac{K D_{f}}{B} ]

(K) is a constant for which values of 0.2 and 0.4 have been proposed. The depth factor correction is applied to (N_0) along with the shape factor correction in Equation 36.1. Once the ultimate bearing capacity is determined, it is corrected by the overburden, giving the net bearing capacity. This is the net pressure the soil can support beyond the pressure applied by the existing overburden.

[ q_{\text{net}} = q_{\text{ult}} - \rho g D_{f} \quad \text{[SI]} \quad (36.3a) ] [ q_{\text{net}} = q_{\text{ut}} - \gamma D_{f} \quad \text{[U.S.]} \quad (36.3b) ]

[ \begin{array}{r l}{{\mathrm{[U.S.]}}}&{{}36.3(b)}\end{array} ]

[ q_{\text{net}} = q_{\text{ult}} - \gamma D_{f} ]

Figure 36.2: Terzaghi Bearing Capacity Factors
sentences: What does the net bearing capacity represent in foundation engineering?, Can anyone explain the difference between ductility and percent elongation?, How do you compute the inverse of a 3x3 matrix?, source_sentence: Backwashing with filtered water pumped back through the filter from the bottom to the top expands the sand layer by 30-50%, which dislodges trapped material. Backwashing for 3-5 minutes at a rate of 8-15 gpm/ft² (5.4-10 L/s-m²) is a typical specification. The head loss is reduced to approximately 1 ft (0.3 m) after washing. Experience has shown that supplementary agitation of the filter media is necessary to prevent "caking" and "mudballs" in almost all installations. Prior to backwashing, the filter material may be expanded by an air prewash volume of 1-8 (2-5 typical) times the sand filter volume per minute for 2-10 minutes (3-5 minutes typical). Alternatively, turbulence in the filter material may be encouraged during backwashing with an air wash or with rotating hydraulic surface jets.

During backwashing, the water in the filter housing will rise at a rate of 1-3 ft/min (0.5-1.5 cm/s). This rise should not exceed the settling velocity of the smallest particle that is to be retained in the filter. The wash water, which is collected in troughs for disposal, constitutes approximately 1-5% of the total processed water. The total water used is approximately 75-100 gal/ft² (3-4 kL/m²). The actual amount of backwash water is given by the equation:

$$ V = A_{\text{filter}} \cdot (\text{rate of rise}) \cdot t_{\text{backwash}} $$

The temperature of the water used in backwashing is important since viscosity changes with temperature (the effect of temperature on water density is negligible). Water at 40°F (4°C) is more viscous than water at 70°F (21°C). Therefore, media particles may be expanded to the same extent using lower upflow rates at lower backwash temperatures. ```markdown

26. Other Filtration Methods

Pressure (sand) filters for water supply treatment operate similarly to rapid sand filters except that incoming water is typically pressurized to 25-75 psig (170-520 kPa gage). Single media filter rates are typically 4-5 gpm/ft² (1.4-14 L/s-m²), with 2-10 gpm/ft² (2.7-3.4 L/s-m² typical), while dual media filters run at 1.5 to 2.0 times these rates. Pressure filters are not used in large installations.

Ultrafilters are membranes that act as sieves to retain turbidity, microorganisms, and large organic molecules that are THM precursors, while allowing water, salts, and small molecules to pass through. Ultrafiltration is effective in removing particles ranging in size of 0.001 to 10 µm. A pressure of 15-75 psig (100-500 kPa) is required to drive the water through the membrane.

Biofilm filtration (biofilm process) uses microorganisms to remove selected contaminants (e.g., aromatics and other hydrocarbons). The operation of biofilters is similar to trickling filters used in wastewater processing. Sand filter facilities are relatively easy to modify—sand is replaced with gravel in the 4-14 mm size range, application rates are decreased, and exposure to chlorine from incoming and backwash water is eliminated. While the maximum may never be used, a maximum backwash rate of 20 gpm/ft² (14 L/s-m²) should be provided for. A µm is the same as a micron.
sentences: How do I calculate the total water used for backwashing?, How do I calculate flow rate if the water depth is 5 ft and channel width is 8 ft?, What is the formula for estimating the percent time spent following on highways?, source_sentence: Here is the LaTeX representation of the angles and the radius of the circle:

\begin{align} \alpha &= \angle PQR \ \beta &= \angle QNR \ \gamma &= \angle RPN \ \end{align}

\begin{align} a &= \text{radius of the circle} \ I &= \text{line segment} \ \end{align}

The figure also includes a dashed line representing a chord and a tangent line from point P to the circle, with a point of tangency labeled 'T'. The tangent line is perpendicular to the radius of the circle at point T.

Figure 79.5 Tangent and Chord Offset Geometry

The short chord distance is

[ \mathrm{NQ} = C = 2R \sin \alpha ] [ \mathrm{NP} = (2R \sin \alpha) \cos \alpha = C \cos \alpha ] [ \mathrm{PQ} = (2R \sin \alpha) \sin \alpha = 2R \sin^2 \alpha ]

\tag{79.23} \tag{79.24} \tag{79.25} ```

7. Curve Layout By Chord Offset

The chord offset method is a third method for laying out horizontal curves. This method is also suitable for short curves. The method is named for the way in which the measurements are made, which is by measuring distances along the main chord from the instrument location at PC.

[ \mathrm{NR} = \mathrm{chord~distance} = \mathrm{NQ}\cos\left({\frac{I}{2}} - \alpha\right) ]

[ \sqrt{2} = (2R\sin\alpha)\cos\left(\frac{I}{2} - \alpha\right) = C\cos\left(\frac{I}{2} - \alpha\right) = (I - \alpha)^{2} ]

[ \mathrm{RQ} = \mathrm{chord~offset} = \mathrm{NQ}~\sin\left({\frac{I}{2}} - \alpha\right) ]

[ = (2R\sin\alpha)\sin\left({\frac{I}{2}} - \alpha\right) = C\sin\left({\frac{I}{2}} - \alpha\right) ]

[ 79.27 ]

8. Horizontal Curves Through Points

Occasionally, it is necessary to design a horizontal curve to pass through a specific point. The following procedure can be used. (Refer to Fig. 79.6.)

Step 1: Calculate ( \alpha ) and ( m ) from ( x ) and ( y ). (If ( x ) and ( m ) are known, skip this step.) [ \alpha = \arctan\left(\frac{y}{x}\right) ] [ m = \sqrt{x^{2} + y^{2}} ]

Step 2: Calculate ( y ). Since ( 90^\circ + \frac{I}{2} + \alpha = 180^\circ ), [ \gamma = 90^\circ - \frac{I}{2} - \alpha ]

Step 3: Calculate ( \phi ). [ \phi = 180^\circ - \arcsin\left(\frac{\sin\gamma}{\cos\left(\frac{I}{2}\right)}\right) ] [ = 180^\circ - \gamma - \phi ]

Step 4: Calculate ( O ). (Refer to Eq. 79.32)
sentences: What's the difference between horizontal and vertical parabolas in their equations?, What does the distance of 50 ft represent in the wave illustration?, What is the relationship between tangent lines and radius in circular geometry?, source_sentence: Description: The image provided is not clear enough to discern any specific details, text, or formulas. It appears to be a blurred image with no distinguishable content. Therefore, I cannot extract any formulas or provide a description of the image content.

Unfortunately, it is extremely difficult to prove compensatory fraud (i.e., fraud for which damages are available). Proving fraud requires showing beyond a reasonable doubt (a) a reckless or intentional misstatement of a material fact, (b) an intention to deceive, (c) it resulted in misleading the innocent party to contract, and (d) it was to the innocent party's detriment. For example, if an engineer claims to have experience in designing steel buildings but actually has none, the court might consider the misrepresentation a fraudulent action. If, however, the engineer has some experience, but an insufficient amount to do an adequate job, the engineer probably will not be considered to have acted fraudulently.

Torts

A tort is a civil wrong committed by one person causing lamage to another person or person's property, emoional well-being, or reputation.11 It is a breach of the ights of an individual to be secure in person or propxty. In order to correct the wrong, a civil lawsuit (tort iction or civil complaint) is brought by the alleged njured party (the plaintiff) against the defendant. To be a valid tort action (i.e., lawsuit), there must have been injury (i.e., damage). Generally, there will be no contract between the two parties, so the tort action annot claim a breach of contract. 12 Cort law is concerned with compensation for the injury, not punishment. Therefore, tort awards usually consist

f general, compensatory, and special damages and arely include punitive and exemplary damages. (See Damages" for definitions of these damages.)

Strict Liability In Tort

itrict liability in tort means that the injured party wins f the injury can be proven. It is not necessary to prove egligence, breach of explicit or implicit warranty, or he existence of a contract (privity of contract). Strict ability in tort is most commonly encountered in prodct liability cases. A defect in a product, regardless of ow the defect got there, is sufficient to create strict ability in tort.

lase law surrounding defective products has developed nd refined the following requirements for winning a trict liability in tort case. The following points must e proved.

The difference between a civil tort (lausuit) and a criminal lausuit is ie alleged injured party. A crime is a wrong against society. A iminal lawsuit is brought by the state against a defendant.

It is possible for an injury to be both a breach of contract and a tort.

ippose an owner has an agreement with a contractor to construct a ilding, and the contract requires the contractor to comply with all ate and federal safety regulations. If the owner is subsequently jured on a stairway because there was no guardrail, the injury could · recoverable both as a tort and as a breach of contract. If a third irty unrelated to the contract was injured, however, that party could cover only through a tort action. · The product was defective in manufacture, design, labeling, and so on.

The product was defective when used.

The defect rendered the product unreasonably dangerous.

The defect caused the injury. .

The specific use of the product that caused the damage was reasonably foreseeable.

Manufacturing And Design Liability
sentences: What factors influence the instantaneous center of rotation in welded structures?, How do you establish if fraud has occurred in a contract?, How do you calculate the probability of multiple events happening?, source_sentence: 9. Area

Equation 9.35 calculates the area, ( A ), bounded by ( x = a ), ( x = b ), ( f_1(x) ) above, and ( f_2(x) ) below. (Note: ( f_2(x) = 0 ) if the area is bounded by the x-axis.) This is illustrated in Fig. 9.1. [ A = \int_{a}^{b} \left( f_{1}(x) - f_{2}(x) \right) \, dx \qquad \qquad (9.35) ] Figure 9.1 Area Between Two Curves

Description: The image shows a graph with two curves labeled f1(x) and f2(x). The graph is plotted on a Cartesian coordinate system with an x-axis and a y-axis. There are two vertical dashed lines intersecting the x-axis at points labeled 'a' and 'b'. The curve f1(x) is above the line y = 0 and the curve f2(x) is below the line y = 0. The area between the two curves from x = a to x = b is shaded, indicating a region of interest or calculation.

The LaTeX representation of the curves is not provided in the image, so I cannot write them in LaTeX form. However, if the curves were described by functions, they could be represented as follows:

f1(x) could be represented as ( f_1(x) = ax^2 + bx + c ) for some constants a, b, and c.

f2(x) could be represented as ( f_2(x) = -ax^2 - bx - c ) for some constants a, b, and c.

The area between the curves from x = a to x = b could be calculated using the integral of the difference between the two functions over the interval [a, b].

Description: The image provided is not clear enough to describe in detail or to extract any formulas. The text is not legible, and no other discernible features can be identified.

Find the area between the x-axis and the parabola ( y = x^2 ) in the interval ([0, 4]).

Description: The image shows a graph with a curve that represents a function y = x^2. There is a vertical dashed line at x = 4, indicating a point of interest or a specific value on the x-axis. The graph is plotted on a Cartesian coordinate system with the x-axis labeled 'x' and the y-axis labeled 'y'. The curve is a parabola that opens upwards, showing that as x increases, y increases at an increasing rate. The point where x = 4 is marked on the x-axis, and the corresponding y-value on the curve is not explicitly shown but can be inferred from the equation y = x^2.

Solution: Referring to Eq. 9.35, [ f_{1}(x) = x^{2} \quad \text{and} \quad f_{2}(x) = 0 ] Thus, [ A = \int_{a}^{b} \left( f_1(x) - f_2(x) \right) dx = \int_{0}^{4} x^2 \, dx = \left[ \frac{x^3}{3} \right]_{0}^{4} = \frac{64}{3} ] ...

10. Arc Length
sentences: Can you show me how to find the area using the integral of the difference of two functions?, Can you explain how to calculate the force BC using trigonometric components?, What is the minimum requirement for steel area in slab reinforcement according to ACI guidelines?
model-index: name: deep learning project
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.2543859649122807
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5789473684210527
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
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name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.7982456140350878
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
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name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.19298245614035087
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.14035087719298245
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.07982456140350876
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.2543859649122807
name: Cosine Recall@1
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value: 0.5789473684210527
name: Cosine Recall@3
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value: 0.7017543859649122
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.7982456140350878
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.5289463979794752
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.4422630650700826
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.45071327302764325
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@1
value: 0.2631578947368421
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5760233918128655
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.695906432748538
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.783625730994152
name: Cosine Accuracy@10
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value: 0.2631578947368421
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value: 0.19200779727095513
name: Cosine Precision@3
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value: 0.13918128654970757
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0783625730994152
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value: 0.2631578947368421
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.5760233918128655
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.695906432748538
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.783625730994152
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.525405284677311
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.4422096908939014
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.45077185641932777
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.260233918128655
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.5526315789473685
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.6754385964912281
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.7573099415204678
name: Cosine Accuracy@10
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name: Cosine Precision@1
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verified: no, type: cosine_ndcg@10
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verified: no, type: cosine_mrr@10
value: 0.4281189083820665
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.4372871346521922
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.2134502923976608
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
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name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
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verified: no, type: cosine_accuracy@10
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name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.39701781193586744
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 64
type: dim_64
metrics: type: cosine_accuracy@1
value: 0.1871345029239766
name: Cosine Accuracy@1
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value: 0.5789473684210527
name: Cosine Recall@5
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name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.42447214920635656
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3461802654785111
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3562882551304709
name: Cosine Map@100
verified: no
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Created: 2024-12-01 07:16:38