Model: DigitalAsocial/all-mpnet-base-v2-ds-rag-5g
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:66424, loss:MultipleNegativesRankingLoss, en, dataset:DigitalAsocial/ds-tb-5-g, 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:66424, loss:MultipleNegativesRankingLoss
widget: source_sentence: If we know p o , our prediction will be heads if p o > 0.5 and tails otherwise.
sentences: Perhaps the most common metric in data science is a binary
variable: click or no-click, buy or don’t buy, fraud or no fraud, and so on., This is because if we choose the more probable case, the probability of error, which is 1 minus the probability of our choice, will be minimum., The precision measures the accuracy of
a predicted positive outcome (see Figure 5-5):
The recall, also known as sensitivity, measures the strength of the model to
predict a positive outcome — the proportion of the 1s that it correctly identifies
(see Figure 5-5)., source_sentence: , K; for example, in optical digit recognition, the input is a bitmap image and there are ten classes.
sentences: The accuracy of predicting the labels of the held-out instances is then reported as
the overall accuracy., The posterior probability of class C i can be calculated as

and for minimum error, the Bayes' classifier chooses the class with the Bayes' classifier highest posterior probability; that is, we

It may be the case that decisions are not equally good or costly., Not all saddle
points are degenerate points and vice versa., source_sentence: How much difference does
changing the value of the smoothing parameter make in your esti-
mate of the 70th percentile of the demand distribution?
sentences: The level of sparsiﬁcation will depend on
the weight threshold at which links are dropped., How much difference does
changing the value of the smoothing parameter make in your esti-
mate of the 70th percentile of the demand distribution?, Classical dimensionality reduction methods like singular value decomposition and
principal component analysis are special cases of neural architectures., source_sentence: (1990) give 4 years of data on monthly demand
for a soft drink.
sentences: 438
10.4.1 Training a Generative Adversarial Network ., Explain how prediction intervals would be computed., If xt is a unit impulse at time t = 0, that is,
xt =
{1,
t = 0
0,
t ≠0
(6.3)
then the output yt is
yt =
∞
∑
i=0
vixt−i = vt
(6.4)

TRANSFER FUNCTION MODELS
429
Therefore the coefficients vi in Eq., source_sentence: All partial derivatives that are greater than the
maximum are set to the maximum threshold.
sentences: Note that this type of clipping does not change the
relative magnitudes of the updates along diﬀerent directions., The
size of this subset is m + m · k. This is where eﬃciency is achieved., library(lubridate)
house$Year = year(house$DocumentDate)
house$Weight = house$Year - 2005
We can compute a weighted regression with the lm function using the weight
argument.
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-5-g
language: en
base_model: sentence-transformers/all-mpnet-base-v2
Likes: 0 | All-time downloads: 18
Created: 2025-12-02 02:43:34