Model: DigitalAsocial/all-mpnet-base-v2-ds-rag-17r
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:193902, loss:MultipleNegativesRankingLoss, en, dataset:DigitalAsocial/ds-tb-17-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:193902, loss:MultipleNegativesRankingLoss
widget: source_sentence: Dividing both sides by 4, taking
(natural) log and rearranging terms, we have
N ≥(4/ϵ) log(4/δ)
(2.7)

30
2
Supervised Learning
x2
x1
C
h
Figure 2.7
The diﬀerence between h and C is the sum of four rectangular strips,
one of which is shaded.
sentences: 2.4
Noise
Noise is any unwanted anomaly in the data and due to noise, the class
noise
may be more diﬃcult to learn and zero error may be infeasible with a
simple hypothesis class (see ﬁgure 2.8)., A rule matches if
binding
there is a set of bindings to the variables existing in the training set., MODEL SPECIFICATION
441
Moreover, since 𝛼t is white noise, the variance of r𝛼𝛽(j) is relatively
easier to obtain than that of rxy(j)., source_sentence: The input h (orange column) and output h′
(cyan column) are split into their constituent dimensions (here four dimensions).
sentences: Output h′
2 is an in-
vertible function of input h2 where the parameters depend on h1., e−λ, where λ is the expected number., Denoting this predicted value by ̂y(i), we may now find the prediction
error for the ith observation as
e(i) = yi −̂y(i)
(3.52)
The prediction error is often called the ith PRESS residual., source_sentence: NOTE
This was just regular Gaussian noise.
sentences: , µN→1} is the tail portion of the base policy, the starting
state of the simulated trajectory is
xk+1 = fk(xk, uk, wk),
and the disturbance sequence {wk, ., Assuming the correct string is located at random in the raw list, we will
have to compare with an average of S/2 strings before we ﬁnd it, which costs
2S/2 binary comparisons; and comparing the correct strings takes N binary
comparisons, giving a total expectation of S + N binary comparisons, if the
strings are chosen at random., But even that is a massive adversarial
weakness., source_sentence: On the other hand, approximation in policy space often relies on
nonlinear programming ideas, and particularly gradient-like descent.
sentences: The policy is assumed to be accessible for sampling, and
it can be used to obtain sample pairs, each consisting of a state and
the control generated by the policy at that state., Solving
for it, we get the support vectors., In principle, any of the methods studied in these ﬁelds can be used in
225

226CHAPTER 9., source_sentence: 180, 181, 185–189, 194
risk Consider a hypothesis h that is used to predict the label y of a data point based on
its features x.
sentences: SOLUTION: The instances that aﬀect the hypothesis are those that are in
the vicinity of instances with a diﬀerent label., Before formally deﬁning these heuristics, we need to have a mech-
anism for formally deﬁning supervised learning problems., We measure the quality of a particular prediction using a loss function
L
(x, y), h

.
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-17-raw
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Created: 2025-12-02 02:56:02