Jordy-VL

Jordy-VL's Stars

• clovaai/donut

  Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022

  Language:Python5.9k49304477

• life4/textdistance

  📐 Compute distance between sequences. 30+ algorithms, pure python implementation, common interface, optional external libs usage.

  Language:Python3.4k640250

• yzhangcs/parser

  :rocket: State-of-the-art parsers for natural language.

  Language:Python83716133143

• PrithivirajDamodaran/FlashRank

  Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.

  Language:Python66772649

• happynear/AMSoftmax

  A simple yet effective loss function for face verification.

  Language:Matlab4873535131

• richzhang/webpage-template

  Simple project webpage template. Originally used in Colorful Image Colorization. ECCV, 2016.

  Language:HTML46230156

• bigscience-workshop/biomedical

  Tools for curating biomedical training data for large-scale language modeling

  Language:Python46030396115

• shabie/docformer

  Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)

  Language:Python259154040

• LxMLS/lxmls-toolkit

  Machine Learning applied to Natural Language Processing Toolkit used in the Lisbon Machine Learning Summer School

  Language:Python2236570215

• Yutong-Zhou-cv/Awesome-Transformer-in-CV

  A Survey on Transformer in CV.

  1896117

• probabilisticai/probai-2022

  Materials of the Nordic Probabilistic AI School 2022.

  Language:Jupyter Notebook1716028

• huggingface/competitions

  Language:Python116303512

• rubenpt91/MP-DocVQA-Framework

  Language:Python544810

• saattrupdan/doubt

  Bringing back uncertainty to machine learning.

  Language:Python503293

• by-liu/MbLS

  Code of our method MbLS (Margin-based Label Smoothing) for network calibration. To Appear at CVPR 2022. Paper : https://arxiv.org/abs/2111.15430

  Language:Python48136

• AIgen/df-posthoc-calibration

  Model-agnostic posthoc calibration without distributional assumptions

  Language:Jupyter Notebook42116

• ZZR8066/GraphDoc

  Language:Python412212

• Impression2805/Awesome-Failure-Detection

  A list of papers that studies out-of-distribution (OOD) detection and misclassification detection (MisD)

  40113

• justindomke/pangolin

  probabilistic programming focused on fun

  Language:Jupyter Notebook353111

• christos42/CLDR_CLNER_models

  Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited. In this paper, we propose a novel contrastive learning framework that trains sentence embeddings to encode the relations in a graph structure. Given a sentence (unstructured text) and its graph, we use contrastive learning to impose relation-related structure on the token level representations of the sentence obtained with a CharacterBERT (El Boukkouri et al., 2020) model. The resulting relation-aware sentence embeddings achieve state-of-the-art results on the relation extraction task using only a simple KNN classifier, thereby demonstrating the success of the proposed method. Additional visualization by a tSNE analysis shows the effectiveness of the learned representation space compared to baselines. Furthermore, we show that we can learn a different space for named entity recognition, again using a contrastive learning objective, and demonstrate how to successfully combine both representation spaces in an entity-relation task.

  Language:Python27203

• fgranese/DOCTOR

  Advances in Neural Information Processing Systems (NeurIPS 2021)

  Language:Python22213

• kartikgupta-at-anu/spline-calibration

  Code implementation of our ICLR'21 paper "Calibration of Neural Networks using Splines"

  Language:Python22232

• lsorber/neo-ls-svm

  Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation

  Language:Python20131

• classifier-calibration/PyCalib

  Python library for classifier calibration

  Language:Python161162

• devmotion/CalibrationErrors.jl

  Estimation of calibration errors.

  Language:Julia15422

• ygjwd12345/VISTA-Net

  The code release for "Variational Structured Attention Networks for Visual Dense Representation Learning"

  Language:Python14205

• timrudner/function-space-empirical-bayes

  Code for the paper 'Function-Space Regularization in Neural Networks: A Probabilistic Perspective'

  Language:Python6201

• euranova/estimating_eces

  Reproducibility content for the paper Estimating Expected Calibration Errors

  Language:Jupyter Notebook4400

• lompabo/acp_summer_school_2023

  Language:Jupyter Notebook4100

• markus93/fit-on-test

  Official repository of the article "On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers"

  Language:Jupyter Notebook1100