Model: DigitalAsocial/all-mpnet-base-v2-ds-rag-17s
Author: DigitalAsocial
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, mpnet, Machine-Learning, Data-Science, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:127058, loss:MultipleNegativesRankingLoss, en, dataset:DigitalAsocial/ds-tb-17-g-sns, arxiv:1908.10084, arxiv:1705.00652, base_model:sentence-transformers/all-mpnet-base-v2, base_model:finetune:sentence-transformers/all-mpnet-base-v2, license:apache-2.0, model-index, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-mpnet-base-v2
Card metadata: tags: Machine-Learning, Data-Science, sentence-transformers, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:127058, loss:MultipleNegativesRankingLoss
widget: source_sentence: Is this an invertible time series? c. What is the mean of the time series?
sentences: Is this a stationary time series process?, We can consider each conditional p(x i |pa i ) to be a factor φ i (x i , pa i ) so that we obtain the Gibbs distribution p(x) = i φ i (x i |pa i )., The difference is that in the case of momentum previous weight changes are remembered, whereas here previous gradient vectors are remembered., source_sentence: Medical researchers strive to overcome the challenge of insufficient datasets by using data-augmentation techniques.
sentences: Figure  11 .1 shows examples of data augmentations commonly used in computer vision., Finding the optimal u (from the perspective of variance maximization) reduces to the following optimization problem:

In this problem it becomes apparent why keeping u unit length is important: if not, u would simply stretch to have infinite length to maximize the objective., It presents new research, relating to rollout algorithms, policy iteration, multiagent systems, partitioned architectures, and distributed asynchronous computation., source_sentence: Therefore, selecting the model that maximizes R 2 is equivalent to selecting the model that minimizes the sum of the squared residuals.
sentences: For example, in designing a forecasting system for inventory control, information may be required on issues such as product shelf life or other aging considerations, the time required to manufacture or otherwise obtain the products (production lead time), and the economic consequences of having too many or too few units of product available to meet customer demand. When multiple products are involved, the level of aggregation of the forecast (e.g., do we forecast individual products or families consisting of several similar products) can be an important consideration. Much of the ultimate success of the forecasting model in meeting the customer expectations is determined in the problem definition phase., Large values of R 2 suggest a good fit to the historical data., Although these estimates might be errorful, they are nevertheless useful in estimating uncertainties., source_sentence: We say two sets A and B are disjoint if A ∩ B = ∅ It turns out that if two sets A and B are disjoint, then we can write the probability of their union as

There is a neat analogy between set algebra and regular algebra.
sentences: This analogy isn't perfect, however., The elbow is the point where the cumulative variance explained flattens out after rising steeply, hence the name of the method., Some time series data exhibit cyclical or seasonal patterns that cannot be effectively modeled using the polynomial model in Eq., source_sentence: We start by multiplying and dividing the log-likelihood by an arbitrary probability distribution q(z) over the latent variables:  (17.14)  We then use Jensen's inequality for the logarithm (equation 17.12) to find a lower bound:  (17.15)  where the right-hand side is termed the evidence lower bound or ELBO. It gets this name because P r(x|ϕ) is called the evidence in the context of Bayes' rule (equation 17.19).
sentences: (The description of this algorithm is based on an unpublished manuscript by Pat Langley.), The neural architecture that computes this quantity is the VAE., To train Flamingo, the authors solve these challenges by foremost exploring ways to generate their own web-scraped multimodal data set similar to existing ones in the language-only domain.
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
language: en
base_model: sentence-transformers/all-mpnet-base-v2
Likes: 0 | All-time downloads: 28
Created: 2025-12-04 00:05:43