How to use bbmb/deep-learning-for-embedding-model-ssilwal-qpham6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bbmb/deep-learning-for-embedding-model-ssilwal-qpham6") sentences = [ "[ 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) ]\n\nVarious 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.\n\nFigure 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.\n\nEquation 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.\n\nSeveral 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:\n\n[ d_{c} = 1 + \\frac{K D_{f}}{B} ]\n\n(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.\n\n[ 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) ]\n\n[ \\begin{array}{r l}{{\\mathrm{[U.S.]}}}&{{}36.3(b)}\\end{array} ]\n\n[ q_{\\text{net}} = q_{\\text{ult}} - \\gamma D_{f} ]\n\nFigure 36.2: Terzaghi Bearing Capacity Factors", "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?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
https://huggingface.co/bbmb/deep-learning-for-embedding-model-ssilwal-qpham6/resolve/main/tokenizer.json
hf download hf://bbmb/deep-learning-for-embedding-model-ssilwal-qpham6/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/bbmb/deep-learning-for-embedding-model-ssilwal-qpham6/resolve/main/tokenizer.json
