ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

Overview

(Comet-) ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

Example for ATOMIC2020

Paper

Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, Yejin Choi
"(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs."
Appearing at AAAI Conference on Artificial Intelligence 2021

Data: ATOMIC 2020

The data for ATOMIC 2020 is available here. If you need the ATOMIC 2018 data ( Sap et al. 2018 ) it is downloadable here.

Model: COMET-ATOMIC 2020

Trained COMET model can be downloaded here.

Codebase

We include code used in expirements in COMET-ATOMIC2020 for reproducibility, ease of use. Our models are based off the HuggingFace Transformers codebase, with minor adjustments to adapt the model for our data. Details can be found in the AAAI paper.

Setup

Run pip install -r requirements.txt to install requirements for your Python instance. We recommend Conda to manage Python installs. Our codebases is on Python 3.

It's recommended that you test that your enviroment is set up correctly before running modeling code. You can do this via python models/comet_atomic2020_gpt2/comet_gpt2.py --test_install

The code for modeling is located in mosaic/infra/modeling. mosaic/datasets/KGDataset is used to convert the ATOMIC2020 CSV into an HuggingFace Datasets object.

Directory Overview

beaker_exp: Contains files needed to run expirements using Beaker (https://beaker.org/) instead of on your local machine.

human_eval: Contains HTML files for human evaluation on Amazon MTurk, as described in the AAAI paper.

models: Contains additional modeling files to reproduce the GPT2 and BART expirements. models/comet_atomic2020_bart contains a README and code to run COMET-BART2020.

scripts: Contains additional scripts (e.g. utils.py) used during expirements in the COMET-ATOMIC2020 paper.

split: Contains code used to make the test, train, and dev splits of ATOMIC2020 with Stratified Random Sampling.

system_eval: Contains code for automatic evaluation of generated entities.

Contributions

We welcome contributions to the codebase of COMET-2020. We encourage pull requests instead of issues; and suggest filing a GitHub issue with questions / suggestions.

License

COMET-ATOMIC 2020 (codebase) is licensed under the Apache License 2.0. The ATOMIC 2020 dataset is licensed under CC-BY.

Contact

Email: jenah[at]allenai[dot]org

WatermarkRemoval-WDNet-WACV2021

WatermarkRemoval-WDNet-WACV2021 Thank you for your attention. Citation Please cite the related works in your publications if it helps your research: @

LUYI 63 Dec 05, 2022
Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Graph Transformer - Pytorch Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by bot

Phil Wang 97 Dec 28, 2022
CSE-519---Project - Job Title Analysis (Project for CSE 519 - Data Science Fundamentals)

A Multifaceted Approach to Job Title Analysis CSE 519 - Data Science Fundamentals Project Description Project consists of three parts: Salary Predicti

Jimit Dholakia 1 Jan 04, 2022
Local Multi-Head Channel Self-Attention for FER2013

LHC-Net Local Multi-Head Channel Self-Attention This repository is intended to provide a quick implementation of the LHC-Net and to replicate the resu

12 Jan 04, 2023
POCO: Point Convolution for Surface Reconstruction

POCO: Point Convolution for Surface Reconstruction by: Alexandre Boulch and Renaud Marlet Abstract Implicit neural networks have been successfully use

valeo.ai 93 Dec 29, 2022
Open-Ended Commonsense Reasoning (NAACL 2021)

Open-Ended Commonsense Reasoning Quick links: [Paper] | [Video] | [Slides] | [Documentation] This is the repository of the paper, Differentiable Open-

(Bill) Yuchen Lin 31 Oct 19, 2022
Python PID Tuner - Makes a model of the System from a Process Reaction Curve and calculates PID Gains

PythonPID_Tuner_SOPDT Step 1: Takes a Process Reaction Curve in csv format - assumes data at 100ms interval (column names CV and PV) Step 2: Makes a r

1 Jan 18, 2022
Python implementation of "Elliptic Fourier Features of a Closed Contour"

PyEFD An Python/NumPy implementation of a method for approximating a contour with a Fourier series, as described in [1]. Installation pip install pyef

Henrik Blidh 71 Dec 09, 2022
Adversarial vulnerability of powerful near out-of-distribution detection

Adversarial vulnerability of powerful near out-of-distribution detection by Stanislav Fort In this repository we're collecting replications for the ke

Stanislav Fort 9 Aug 30, 2022
Simulator for FRC 2022 challenge: Rapid React

rrsim Simulator for FRC 2022 challenge: Rapid React out-1.mp4 Usage In order to run the simulator use the following: python3 rrsim.py [config_path] wh

1 Jan 18, 2022
Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21)

Learning Structural Edits via Incremental Tree Transformations Code for "Learning Structural Edits via Incremental Tree Transformations" (ICLR'21) 1.

NeuLab 40 Dec 23, 2022
This program can detect your face and add an Christams hat on the top of your head

Auto_Christmas This program can detect your face and add a Christmas hat to the top of your head. just run the Auto_Christmas.py, then you can see the

3 Dec 22, 2021
TransVTSpotter: End-to-end Video Text Spotter with Transformer

TransVTSpotter: End-to-end Video Text Spotter with Transformer Introduction A Multilingual, Open World Video Text Dataset and End-to-end Video Text Sp

weijiawu 66 Dec 26, 2022
Paper list of log-based anomaly detection

Paper list of log-based anomaly detection

Weibin Meng 411 Dec 05, 2022
Comp445 project - Data Communications & Computer Networks

COMP-445 Data Communications & Computer Networks Change Python version in Conda

Peng Zhao 2 Oct 03, 2022
Malware Env for OpenAI Gym

Malware Env for OpenAI Gym Citing If you use this code in a publication please cite the following paper: Hyrum S. Anderson, Anant Kharkar, Bobby Fila

ENDGAME 563 Dec 29, 2022
The repository includes the code for training cell counting applications. (Keras + Tensorflow)

cell_counting_v2 The repository includes the code for training cell counting applications. (Keras + Tensorflow) Dataset can be downloaded here : http:

Weidi 113 Oct 06, 2022
Code for "Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans" CVPR 2021 best paper candidate

News 05/17/2021 To make the comparison on ZJU-MoCap easier, we save quantitative and qualitative results of other methods at here, including Neural Vo

ZJU3DV 748 Jan 07, 2023
An NVDA add-on to split screen reader and audio from other programs to different sound channels

An NVDA add-on to split screen reader and audio from other programs to different sound channels (add-on idea credit: Tony Malykh)

Joseph Lee 7 Dec 25, 2022