Model: noystl/recomb-pred-bge-large-en
Author: noystl
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:784827, loss:ContrastiveLoss, en, dataset:noystl/Recombination-Pred, arxiv:2505.20779, arxiv:1908.10084, base_model:BAAI/bge-large-en-v1.5, base_model:finetune:BAAI/bge-large-en-v1.5, license:cc, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-large-en-v1.5
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:784827, loss:ContrastiveLoss
base_model: BAAI/bge-large-en-v1.5
widget: source_sentence: Represent this sentence for searching relevant passages: Existing methods for anomaly detection on dynamic graphs struggle with capturing complex time information in graph structures and generating effective negative samples for unsupervised learning. These challenges highlight the need for improved methodologies that can address the limitations of current approaches in this field.We suggest combining 'a message-passing framework' and 
sentences: a single global model, videos, sequential polygon generation, source_sentence: Represent this sentence for searching relevant passages: The study addresses the need for effective tools that allow both novice and expert users to analyze the diversity of news coverage about events. It highlights the importance of tailoring the interface to accommodate non-expert users while also considering the insights of journalism-savvy users, indicating a gap in existing systems that cater to varying levels of expertise in news analysis.We suggest combining 'a coordinated visualization interface tailored for visualization non-expert users' and 
sentences: worst-case resource analysis, Graph Convolution Networks, a text encoder, source_sentence: Represent this sentence for searching relevant passages: The accuracy of pixel flows is crucial for achieving high-quality video enhancement, yet most prior works focus on estimating dense flows that are generally less robust and computationally expensive. This highlights a gap in existing methodologies that fail to prioritize accuracy over density, necessitating a more efficient approach to flow estimation for video enhancement tasks.We suggest combining 'sparse point cloud data' and 
sentences: a Temporal Eigenvalue Loss, diffusion models, explicit 3D representations, such as polygonal meshes, source_sentence: Represent this sentence for searching relevant passages: The traditional frame of discernment lacks a crucial factor, the sequence of propositions, which limits the effectiveness of existing methods to measure uncertainty. This gap highlights the need for a more comprehensive approach that can better represent the relationships between the elements of the frame of discernment.We suggest 'combine the order of propositions and the mass of them' inspired by 
sentences: the traditional matching-optimization methods where matching is introduced to handle large displacements before energy-based optimizations, encoder-decoder models, In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable, source_sentence: Represent this sentence for searching relevant passages: The study addresses the need for effective time series forecasting methods to estimate the spread of epidemics, particularly in light of the resurgence of COVID-19 cases. It highlights the importance of accurately modeling both linear and non-linear features of epidemic data to provide state authorities and health officials with reliable short-term forecasts and strategies.We suggest combining 'ARIMA' and 
sentences: Transformers, a traditional feature-mixed branch, the human brain is able to efficiently learn effective control strategies using limited resources
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: cc
datasets: noystl/Recombination-Pred
language: en
Likes: 0 | All-time downloads: 14
Created: 2025-04-11 12:38:36