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
Pytorch Implementation for (STANet+ and STANet)

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SAT: 2D Semantics Assisted Training for 3D Visual Grounding, ICCV 2021 (Oral)

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Notebooks, slides and dataset of the CorrelAid Machine Learning Winter School

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Jianwei ZHANG 8 Oct 14, 2021
Supervised Classification from Text (P)

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Anthony Scopatz 67 Dec 24, 2022
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VGGVox models for speaker identification and verification This directory contains code to import and evaluate the speaker identification and verificat

338 Dec 27, 2022
Audio Source Separation is the process of separating a mixture into isolated sounds from individual sources

Audio Source Separation is the process of separating a mixture into isolated sounds from individual sources (e.g. just the lead vocals).

Victor Basu 14 Nov 07, 2022
Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
LBK 20 Dec 02, 2022
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

PyStan NOTE: This documentation describes a BETA release of PyStan 3. PyStan is a Python interface to Stan, a package for Bayesian inference. Stan® is

Stan 229 Dec 29, 2022
A Python implementation of global optimization with gaussian processes.

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fernando 6.5k Jan 02, 2023
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Decision AI 25 Dec 23, 2022
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FuxiCV 78 Dec 29, 2022
Fuzzing JavaScript Engines with Aspect-preserving Mutation

DIE Repository for "Fuzzing JavaScript Engines with Aspect-preserving Mutation" (in S&P'20). You can check the paper for technical details. Environmen

gts3.org (<a href=[email protected])"> 190 Dec 11, 2022
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intenti

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QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.

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