Release of the ConditionalQA dataset

Overview

ConditionalQA

Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers.

Disclaimer

This dataset should ONLY be used for NLP research purpose. Answers are NOT verified by legal professionals and should NOT be used for any legal purposes.

Evaluate

Please generate your predictions using the format sample_output.json. Run the following command to evaluate your predictions with evaluate.py:

python evaluate.py --pred_file=sample_output.json --ref_file=v1_0/dev.json

Leaderboard

Submit your predictions to the Leaderboard.

Please email your Codalab username to [email protected] if you would like your results to be added to the leaderboard. Include your organisation, a link to your paper, and a short description of your model in the email.

Citation

If you use these datasets please cite the following:

TBD
Generate images from texts. In Russian. In PaddlePaddle

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bulbea "Deep Learning based Python Library for Stock Market Prediction and Modelling." Table of Contents Installation Usage Documentation Dependencies

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28 Jan 03, 2023
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89 Nov 14, 2022
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WIDER-YOLO : Yüz Tespit Uygulaması Yap Wider-Yolo Kütüphanesinin Kullanımı 1. Wider Face Veri Setini İndir Train Dataset Val Dataset Test Dataset Not:

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Simple reference implementation of GraphSAGE.

Reference PyTorch GraphSAGE Implementation Author: William L. Hamilton Basic reference PyTorch implementation of GraphSAGE. This reference implementat

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Plugin adapted from Ultralytics to bring YOLOv5 into Napari

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2 May 05, 2022
Bootstrapped Representation Learning on Graphs

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DSpaceDL A tool for downloading files from DSpace items. For some reason, DSpace systems have a dogshit UI, and Universities absolutely LOOOVE to use

Soumitra Shewale 5 Dec 01, 2022
maximal update parametrization (µP)

Maximal Update Parametrization (μP) and Hyperparameter Transfer (μTransfer) Paper link | Blog link In Tensor Programs V: Tuning Large Neural Networks

Microsoft 694 Jan 03, 2023
Code of the paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodner and Joachim Denzler

Part Detector Discovery This is the code used in our paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodne

Computer Vision Group Jena 17 Feb 22, 2022
SegNet-Basic with Keras

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Yad Konrad 81 Jun 30, 2022
Facial Image Inpainting with Semantic Control

Facial Image Inpainting with Semantic Control In this repo, we provide a model for the controllable facial image inpainting task. This model enables u

Ren Yurui 8 Nov 22, 2021
The self-supervised goal reaching benchmark introduced in Discovering and Achieving Goals via World Models

Lexa-Benchmark Codebase for the self-supervised goal reaching benchmark introduced in 'Discovering and Achieving Goals via World Models'. Setup Create

1 Oct 14, 2021
BARF: Bundle-Adjusting Neural Radiance Fields 🤮 (ICCV 2021 oral)

BARF 🤮 : Bundle-Adjusting Neural Radiance Fields Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey IEEE International Conference on Comp

Chen-Hsuan Lin 539 Dec 28, 2022