Model: DigitalAsocial/all-mpnet-base-v2-ds-rag-5r
Author: DigitalAsocial
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, mpnet, Data-Science, Machine-Learning, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:67416, loss:MultipleNegativesRankingLoss, en, dataset:DigitalAsocial/ds-tb-5-raw, 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: Data-Science, Machine-Learning, sentence-transformers, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:67416, loss:MultipleNegativesRankingLoss
widget: source_sentence: , k on their diagonal and zero elsewhere.
sentences: The sample ACF and PACF of the data for that time period in Figure 6.17

466
TRANSFER FUNCTIONS AND INTERVENTION MODELS
100
80
60
Week
Week 88
40
20
0
100,000
160,000
140,000
Sales
200,000
180,000
220,000
120,000
FIGURE 6.16
Time series plot of the weekly sales data., Just as in equation 6.10, we can write
X = u1a1vT
1 + u2a2vT
2 + · · · + ukakvT
k
(6.29)
We can ignore the corresponding ui, vi of very small, though nonzero,
ai and can still reconstruct X without too much error., 13.8
Multiple Kernel Learning
It is possible to construct new kernels by combining simpler kernels., source_sentence: The main diﬀerence is that a node appears at most once as a neighbor of an-
other node, whereas a word might appear more than once in the context of another word.
sentences: 3.2.6
A Decoupled View of Vector-Centric Backpropagation
In the previous discussion, two equivalent ways of computing the updates based on Equa-
tions 3.12 and 3.18 were provided., (4.59),
we obtain
eT −(1 −𝜆)eT−1 = (yT −̂yT−1) −(1 −𝜆)(yT−1 −̂yT−2)
= yT −yT−1 −̂yT−1 + 𝜆yT−1 + (1 −𝜆)̂yT−2
⏟⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏟⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏞⏟
=̂yT−1
= yT −yT−1 −̂yT−1 + ̂yT−1
= yT −yT−1., 8This fact is not evident in the toy example of Figure 2.17., source_sentence: This inﬂuence is speciﬁed by the conditional probability
P(Y|X).
sentences: Seasonality
is the component of time series behavior that repeats on a regular basis,
such as each year., Note that one of the
classes is deﬁned by strongly non-zero values in the ﬁrst and third dimensions, whereas the
second class is deﬁned by strongly non-zero values in the second and fourth dimensions., The nodes and the arcs between the nodes deﬁne the struc-
ture of the network, and the conditional probabilities are the parameters
given the structure., source_sentence: 238
9
Decision Trees
Rokach, L., and O. Maimon.
sentences: “Top-Down Induction of Decision Trees
Classiﬁers—A Survey.” IEEE Transactions on Systems, Man, and Cybernetics–
Part C 35:476–487., The only feedback is at the
end of the game when we win or lose the game., Subsequently, this computation is propagated
in the backwards direction with dynamic programming updates (similar to Equation 3.8)., source_sentence: Therefore, one can use L1-regularization
to estimate which features are predictive to the application at hand.
sentences: Blumer, A., A. Ehrenfeucht, D. Haussler, and M. K. Warmuth., What about the connections in the hidden layers whose weights are set to 0?, In cases where computational complexity is important, such
as in a production setting where thousands of models are being fit, it may not be
worth the extra computational effort.
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
language: en
base_model: sentence-transformers/all-mpnet-base-v2
datasets: DigitalAsocial/ds-tb-5-raw
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Created: 2025-12-02 02:50:10