Model: DigitalAsocial/all-MiniLM-L6-v2-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:134200, loss:MultipleNegativesRankingLoss, en, dataset:DigitalAsocial/ds-tb-17-g-sns-aml, arxiv:1908.10084, arxiv:1705.00652, base_model:sentence-transformers/all-MiniLM-L6-v2, base_model:finetune:sentence-transformers/all-MiniLM-L6-v2, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-MiniLM-L6-v2
Card metadata: tags: Machine-learning, Data-Science, sentence-transformers, sentence-similarity, feature-extraction, dense, generated_from_trainer, dataset_size:134200, loss:MultipleNegativesRankingLoss
widget: source_sentence: Currently, there seems to be breaking transformer news nearly every week with no sign of slowing.
sentences: Another active research aspect is concerned with improving the understanding of transformer-based models to further advance them., For convenience, we choose to use a vector of unit length (its norm is 1) and obtain this by dividing w by its norm, w ∥w∥ ., Measure the error (log Z Plog Z Q ) in bits., source_sentence: Using the first three years of data, develop an appropriate ARIMA model and a procedure for these data.
sentences: Recall that the agent makes a decision at times determined by external events (or by other parts of the robot's control system)., A related fact is that in approximation in value space with multistep lookahead, J μ is the result of a step of Newton's method that starts at the function obtained by applying multiple value iterations to J., c. Make one-step-ahead forecasts of the last 12 months., source_sentence: 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]).
sentences: Sometimes the alternative notation p(j | i, u) is used for the transition probabilities. In the artificial intelligence literature, the focus is primarily on finitestate MDPs, particularly discounted and stochastic shortest path infinite horizon problems., Research on artificial neural networks is as old as the digital computer., Then the summations u P (u)f (u) should be written x P (x)f (u(x)). This means that P (u) is a finite sum of delta functions. This restriction guarantees that the mean and variance of u do exist, which is not necessarily the case for general P (u)., source_sentence: But how small can the error probability be?
sentences: The results described above allow us to offer the following service: if he tells us the properties of his channel, the desired rate R and the desired error probability p B , we can, after working out the relevant functions C, E r (R), and E sp (R), advise him that there exists a solution to his problem using a particular blocklength N ; indeed that almost any randomly chosen code with that blocklength should do the job., If the problem space is complex, a few base-classifiers may be cascaded increasing the complexity at each stage., Based on the fact that all these major implementations use normalization, it is clearly important, but why not just used standard batch normalization? Unfortunately, batch normalization is too memory intensive at our resolution. We have to come up with something that allows us to work with a few examples-that fit into our GPU memory with the two network graphs-but still works well., source_sentence: If the weights are independent and the prior is taken as Gaussian, N (0, 1/2λ)

the MAP estimate minimizes the augmented error function

where E is the usual classification or regression error (negative log likelihood).
sentences: This approach of removing unnecessary parameters is known as ridge regression in statistics., If reselecting the test data doesn't help, you have other generalization problems., These architectures are used across supervised, unsupervised, and reinforcement learning.
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-aml
language: en
base_model: sentence-transformers/all-MiniLM-L6-v2
Likes: 0 | All-time downloads: 21
Created: 2025-12-22 23:57:27