CodeContests is a competitive programming dataset for machine-learning

Overview

CodeContests

CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode.

It consists of programming problems, from a variety of sources:

Site URL Source
Aizu https://judge.u-aizu.ac.jp CodeNet
AtCoder https://atcoder.jp CodeNet
CodeChef https://www.codechef.com description2code
Codeforces https://codeforces.com description2code and Codeforces
HackerEarth https://www.hackerearth.com description2code

Problems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.

Usage

Install the Cloud SDK, which provides the gsutil utility. You can then download the full data (~3GiB) with, e.g:

gsutil -m cp -r gs://dm-code_contests /tmp

The data consists of ContestProblem protocol buffers in Riegeli format. See contest_problem.proto for the protocol buffer definition and documentation of its fields.

The dataset contains three splits:

Split Filename
Training code_contests_train.riegeli-*-of-00128
Validation code_contests_valid.riegeli
Test code_contests_test.riegeli

There is example code for iterating over the dataset in C++ (in print_names.cc) and Python (in print_names_and_sources.py). For example, you can print the source and name of each problem in the validation data by installing bazel and then running:

bazel run -c opt \
  :print_names_and_sources /tmp/dm-code_contests/code_contests_valid.riegeli

Or do the same for the training data with the following command (which will print around 13000 lines of output):

bazel run -c opt \
  :print_names_and_sources /tmp/dm-code_contests/code_contests_train.riegeli*

Planned updates

We plan to update this repository with code for executing and evaluating potential solutions.

Citing this work

If you use this dataset or code, please cite this paper:

@misc{alphacode,
    title={Competition-Level Code Generation with AlphaCode},
    author={Li, Yujia and Choi, David and Chung, Junyoung and Kushman, Nate and
    Schrittwieser, Julian and Leblond, Rémi and Eccles, Tom and
    Keeling, James and Gimeno, Felix and Dal Lago, Agustin and
    Hubert, Thomas and Choy, Peter and de Masson d'Autume, Cyprien and
    Babuschkin, Igor and Chen, Xinyun and Huang, Po-Sen and Welbl, Johannes and
    Gowal, Sven and Cherepanov, Alexey and Molloy, James and
    Mankowitz, Daniel and Sutherland Robson, Esme and Kohli, Pushmeet and
    de Freitas, Nando and Kavukcuoglu, Koray and Vinyals, Oriol},
    year={2022},
    month={Feb}}

License

The code is licensed under the Apache 2.0 License.

All non-code materials provided are made available under the terms of the CC BY 4.0 license (Creative Commons Attribution 4.0 International license).

We gratefully acknowledge the contributions of the following:

Use of the third-party software, libraries code or data may be governed by separate terms and conditions or license provisions. Your use of the third-party software, libraries or code may be subject to any such terms. We make no representations here with respect to rights or abilities to use any such materials.

Disclaimer

This is not an official Google product.

Owner
DeepMind
DeepMind
Scikit-learn compatible estimation of general graphical models

skggm : Gaussian graphical models using the scikit-learn API In the last decade, learning networks that encode conditional independence relationships

213 Jan 02, 2023
Constrained Language Models Yield Few-Shot Semantic Parsers

Constrained Language Models Yield Few-Shot Semantic Parsers This repository contains tools and instructions for reproducing the experiments in the pap

Microsoft 43 Nov 23, 2022
Code used for the results in the paper "ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning"

Code used for the results in the paper "ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning" Getting started Prerequisites CUD

70 Dec 02, 2022
Fuse radar and camera for detection

SAF-FCOS: Spatial Attention Fusion for Obstacle Detection using MmWave Radar and Vision Sensor This project hosts the code for implementing the SAF-FC

ChangShuo 18 Jan 01, 2023
Generating Videos with Scene Dynamics

Generating Videos with Scene Dynamics This repository contains an implementation of Generating Videos with Scene Dynamics by Carl Vondrick, Hamed Pirs

Carl Vondrick 706 Jan 04, 2023
MG-GCN: Scalable Multi-GPU GCN Training Framework

MG-GCN MG-GCN: multi-GPU GCN training framework. For more information, please read our paper. After cloning our repository, run git submodule update -

Translational Data Analytics (TDA) Lab @GaTech 6 Oct 24, 2022
Official Implementation of CVPR 2022 paper: "Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning"

(CVPR 2022) Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning ArXiv This repo contains Official Implementat

Yujun Shi 24 Nov 01, 2022
Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

flownet2-pytorch Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. Multiple GPU training is supported, a

NVIDIA Corporation 2.8k Dec 27, 2022
Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).

Adaptive Segmentation Mask Attack This repository contains the implementation of the Adaptive Segmentation Mask Attack (ASMA), a targeted adversarial

Utku Ozbulak 53 Jul 04, 2022
Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Summary Explorer Summary Explorer is a tool to visually inspect the summaries from several state-of-the-art neural summarization models across multipl

Webis 42 Aug 14, 2022
Nicholas Lee 3 Jan 09, 2022
YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

Adam Van Etten 145 Jan 01, 2023
ICML 21 - Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Voice2Series-Reprogramming Voice2Series: Reprogramming Acoustic Models for Time Series Classification International Conference on Machine Learning (IC

49 Jan 03, 2023
Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

Period-alternatives-of-Softmax Experimental Demo for our paper 'Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechani

slwang9353 0 Sep 06, 2021
The official implementation of "Rethink Dilated Convolution for Real-time Semantic Segmentation"

RegSeg The official implementation of "Rethink Dilated Convolution for Real-time Semantic Segmentation" Paper: arxiv D block Decoder Setup Install the

Roland 61 Dec 27, 2022
Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face Manipulation" published in CVPR 2020.

FFD Source Code Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face M

88 Nov 22, 2022
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

Deepak Nandwani 1 Jan 01, 2022
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper

phoenix 3 Feb 27, 2022
Node for thenewboston digital currency network.

Project setup For project setup see INSTALL.rst Community Join the community to stay updated on the most recent developments, project roadmaps, and ra

thenewboston 27 Jul 08, 2022
This is an open source library implementing hyperbox-based machine learning algorithms

hyperbox-brain is a Python open source toolbox implementing hyperbox-based machine learning algorithms built on top of scikit-learn and is distributed

Complex Adaptive Systems (CAS) Lab - University of Technology Sydney 21 Dec 14, 2022