Model: DigitalAsocial/bge-base-en-v1.5-ds-rag-s
Author: DigitalAsocial
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, Machine-learning, Data-Science, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:40806, loss:MultipleNegativesRankingLoss, en, dataset:DigitalAsocial/ds-tb-17-g-sns-bge, arxiv:1908.10084, 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: tags: Machine-learning, Data-Science, sentence-transformers, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:40806, loss:MultipleNegativesRankingLoss
widget: source_sentence: From the graph in Figure  5 .1, we see that for π z = {q, h} we have z ⊥ ⊥ pre z \ π z |π z . Note that π z = {q} does not work because z ⊥ ⊥ e, h|q does not hold. (Third graph in Figure  5 .2.)
sentences: • We consider a: pre a = {e, h, q, z}. This is the last node in the ordering. To find the minimal set π a for which a ⊥ ⊥ pre a \ π a |π a , we can determine its Markov blanket MB(a). The Markov blanket is the set of parents (none), children (q), and co-parents of a (z) in Since the skeleton in the obtained minimal I-map is different from the skeleton of G, we do not have I-equivalence. Note that the ordering (e, h, q, z, a) yields a denser graph (Figure  5 .2) than the graph in Figure  5 .1. Whilst a minimal I-map, the graph does e.g. Furthermore, the causal interpretation of the two graphs is different. (b) For the collection of random variables (a, z, h, q, e) you are given the following Markov blankets for each variable:

• MB(a) = {q,z} • MB(z) = {a,q,h} • MB(h) = {z} • MB(q) = {a,z,e} • MB(e) = {q} (i) Draw the undirected minimal I-map representing the independencies., We now study these two models of variation in detail., After the input sequence is processed, it is added to the hidden state and passed forward in time through the recurrent connections between the hidden states in the encoder. Despite the context vector usually being a simple function of the last hidden state, its role cannot be underestimated. Specifically, the encoded state summarizes important information from the input sequence, e.g. the intent in a question answering task or the meaning of a text in the case of machine translation. After the context is passed to every hidden state of the decoder, the decoder RNN uses this information to produce the target sequence of length n y , which can of course vary from n x . At the latest through the above illustration, it is clear that the decoder is particularly interesting to look at in the form of equations. The notation mainly follows  Cho et al. The decoder is another type of RNN which is trained to predict the target based on the hidden state at the last time step., source_sentence: In section 18.2, we consider the encoder in detail. Its properties are non-obvious but are critical for the learning algorithm. In section 18.3, we discuss the decoder. Section 18.4 derives the training algorithm, and section 18.5 reformulates it to be more practical. Section 18.6 discusses implementation details, including how to make the generation conditional on text prompts. The diffusion or forward process foot_7  (figure  18 .2) maps a data example x through a series of intermediate variables z 1 , z 2 , .
sentences: CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideas, as well as a software tool to generate a large and diverse number of test cases quickly. To break down potential capability failures into specific behaviors, CheckList introduces three different test types. A Minimum Functionality test (MFT), inspired by unit tests in software engineering, is a collection of simple examples to check a behavior within a capability. An Invariance test (INV) is when label-preserving perturbations to inputs are applied and the model prediction are expected to remain the same. A Directional Expectation test (DIR) is similar, except that the label is expected to change in a certain way. Tests created with CheckList can be applied to any model, making it easy to incorporate in current benchmarks or evaluation pipelines and CheckList is open source., (  Grinstead and Snell, 1997; Jaynes, 2003)  that introduce the three concepts of sample space, event space, and probability measure., The hyperparameters β t ∈ [0, 1] determine how quickly the noise is blended and are collectively known as the noise schedule. The forward process can equivalently be written as: ., source_sentence: None of these can be ascertained by scrutinizing code.
sentences: Of course in the multiagent case, the onecomponent-at-a-time implementation has an additional favorable property: it can be viewed as rollout (without simplification) for a modified but equivalent DP problem (see Section 1.6.7). An important variation of rollout algorithms is truncated rollout with terminal cost approximation. Here the rollout trajectories are obtained by running the base policy from the leaf nodes of the lookahead tree, but they are truncated after a given number of steps, while a terminal cost approximation is added to the heuristic cost to compensate for the resulting error. This is important for problems with a large number of stages, and it is also essential for infinite horizon problems where the rollout trajectories have infinite length. One possibility that works well for many problems is to simply set the terminal cost approximation to zero. Alternatively, the terminal cost function approximation may be obtained by using some sophisticated offline training process that may involve an approximation architecture such as a neural network, or by using some heuristic calculation based on a simplified version of the problem. This form of truncated rollout may also be viewed as an intermediate approach between standard rollout where there is no truncation (and hence no cost function approximation), and approximation in value space without any rollout. We will now consider a rollout algorithm for discrete deterministic optimization for the case where we do not know the cost function and the constraints of the problem., However, it does not exhibit structural transparency as we do not have access to the code, and it does not exhibit run transparency as we have no access to the learned parameters, hardware, or training data. The subsequent version GPT4 is not transparent at all. The details of how this commercial product works are unknown. Even if a system is transparent, this does not imply that we can understand how a decision is made or what information this decision is based on. Deep networks may contain billions of parameters, so there is no way we can understand how they work based on examination alone. However, in some jurisdictions, the public may have a right to an explanation., We'll start with count-based approaches., source_sentence: This is the U-Net architecture. This architecture is simpler to write than the ResNet architecture, which some implementations use. Within our Generator function we first define the helper functions:

1 Define the conv2d() function as follows:

a Standard 2D convolutional layer b Leaky ReLU activation c Instance normalization 8 2 Define the deconv2d() function as a transposed 9 convolution (aka deconvolution) layer that does the following: a Upsamples the input_layer b Possibly applies dropout if we set the dropout rate c Always applies InstanceNormalization d More importantly, creates a skip connection between its output layer and the layer of corresponding dimensionality from the downsampling part from figure 9.4 g_loss = self.combined.train_on_batch([imgs_A, imgs_B], [valid, valid, imgs_A, imgs_B, imgs_A, imgs_B]) if batch_i % sample_interval == 0: self.sample_images(epoch, batch_i)

We have written all of this complicated code and are now ready to instantiate a Cycle-GAN object and look at some results, from the sampled images: gan = CycleGAN() gan.train(epochs=100, batch_size=64, sample_interval=10)

When you run these results, we hope you will be as impressed as we were.
sentences: Because of the absolutely astonishing results, lots of researchers flocked to improve on the technique., Furthermore, since the likelihood term for linear regression with Gaussian noise is Gaussian, the negative log-likelihood for each example is a squared error function., Generate another 100 observations of an AR(1) time series and add these observations to the original time series. Calculate the sample ACF and sample PACF of the new time series. Identify the model form and estimate the parameters. 7.23 Generate 100 observations of an MA(2) time series., source_sentence: The more recent works of Sutton et al. [SMS99], Baxter and Bartlett  [BaB01] , Konda and Tsitsiklis  [KoT99] ,  [KoT03] , Marbach and Tsitsiklis  [MaT01] ,  [MaT03] , Peters and Schaal  [PeS08] , and Bhatnagar et al. [BSG09]  have been influential; see also the survey by Grondman et al. Actor-critic algorithms that are suitable for POMDP and involve gradient estimation have been given by H. Yu [Yu05], and Estanjini, Li, and Paschalidis  [ELP12] .
sentences: Random search methods: The cross-entropy method was initially developed in the context of rare event simulation and was later adapted for use in optimization. For textbook accounts, see Rubinstein and Kroese  [RuK04] ,  [RuK13] ,  [RuK16] , and Busoniu et al., 3.8 Using the Gram-Schmidt method, turn the basis B = (b 1 , b 2 ) of a twodimensional subspace U ⊆ R 3 into an ONB C = (c 1 , c 2 ) of U , where

3.9 Let n ∈ N and let x 1 , . , xn > 0 be n positive real numbers so that x 1 + ., This book adheres to the "standard" notation that emerged during the Bellman/Pontryagin optimal control era; see e.g., the books by Athans and Falb [AtF66], Bellman [Bel67], and Bryson and Ho [BrH75]. This notation is consistent with the author's other DP books and is the most appropriate for a unified treatment of the subject, which simultaneously addresses discrete and continuous spaces problems. A summary of the most prominently used symbols in our notational system is as follows:

(a) x: state (also i for finite-state systems). For deterministic systems,

and for stochastic systems,

Also f k in place of f for time-varying systems. (f) g: cost per stage [g(x, u) for deterministic systems, and g(x, u, w) for stochastic systems; also g k in place of g for time-varying systems]. (g) p xy (u): transition probability from state x to state y under control u in finite-state systems [also p ij (u)]. (h) α: discount factor in discounted problems. The x-u-J notation is standard in deterministic optimal control textbooks (e.g., the classical books [AtF66] and [BrH75], noted earlier, as well as the more recent books by Stengel [Ste94], Kirk [Kir04], and Liberzon [Lib11]).
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: pearson_cosine, spearman_cosine
model-index: name: SentenceTransformer
results: task: type: semantic-similarity
name: Semantic Similarity
dataset: name: val
type: val
metrics: type: pearson_cosine
name: Pearson Cosine
verified: no, type: spearman_cosine
name: Spearman Cosine
verified: no
license: apache-2.0
datasets: DigitalAsocial/ds-tb-17-g-sns-bge
language: en
base_model: BAAI/bge-base-en-v1.5
Likes: 0 | All-time downloads: 21
Created: 2025-12-23 00:24:11