Code and models used in "MUSS Multilingual Unsupervised Sentence Simplification by Mining Paraphrases".

Related tags

Deep Learningmuss
Overview

Multilingual Unsupervised Sentence Simplification

Code and pretrained models to reproduce experiments in "MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases".

Prerequisites

Linux with python 3.6 or above.

Installing

git clone [email protected]:facebookresearch/muss.git
cd muss/
pip install -e .

How to use

Some scripts might still contain a few bugs, if you notice anything wrong, feel free to open an issue or submit a Pull Request.

Simplify sentences from a file using pretrained models

# English
python scripts/simplify.py scripts/examples.en --model-name muss_en_wikilarge_mined
# French
python scripts/simplify.py scripts/examples.fr --model-name muss_fr_mined
# French
python scripts/simplify.py scripts/examples.es --model-name muss_es_mined

Pretrained models should be downloaded automatically, but you can also find them here:
muss_en_wikilarge_mined
muss_en_mined
muss_fr_mined
muss_es_mined

Mine the data

python scripts/mine_sequences.py

Train the models

python scripts/train_model.py

Evaluate simplifications

Please head over to EASSE for Sentence Simplification evaluation.

License

The MUSS license is CC-BY-NC. See the LICENSE file for more details.

Authors

Citation

If you use MUSS in your research, please cite MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases

@article{martin2021muss,
  title={MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases},
  author={Martin, Louis and Fan, Angela and de la Clergerie, {\'E}ric and Bordes, Antoine and Sagot, Beno{\^\i}t},
  journal={arXiv preprint arXiv:2005.00352},
  year={2021}
}
Owner
Facebook Research
Facebook Research
Deep-learning-roadmap - All You Need to Know About Deep Learning - A kick-starter

Deep Learning - All You Need to Know Sponsorship To support maintaining and upgrading this project, please kindly consider Sponsoring the project deve

Instill AI 4.4k Dec 26, 2022
Integrated physics-based and ligand-based modeling.

ComBind ComBind integrates data-driven modeling and physics-based docking for improved binding pose prediction and binding affinity prediction. Given

Dror Lab 44 Oct 26, 2022
Instance-wise Occlusion and Depth Orders in Natural Scenes (CVPR 2022)

Instance-wise Occlusion and Depth Orders in Natural Scenes Official source code. Appears at CVPR 2022 This repository provides a new dataset, named In

27 Dec 27, 2022
An Object Oriented Programming (OOP) interface for Ontology Web language (OWL) ontologies.

Enabling a developer to use Ontology Web Language (OWL) along with its reasoning capabilities in an Object Oriented Programming (OOP) paradigm, by pro

TheEngineRoom-UniGe 7 Sep 23, 2022
Package for working with hypernetworks in PyTorch.

Package for working with hypernetworks in PyTorch.

Christian Henning 71 Jan 05, 2023
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Status: Under development (expect bug fixes and huge updates) ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectiv

37 Dec 28, 2022
Activity image-based video retrieval

Cross-modal-retrieval Our approach is focus on Activity Image-to-Video Retrieval (AIVR) task. The compared methods are state-of-the-art single modalit

BCMI 75 Oct 21, 2021
Implementation of the paper titled "Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees"

Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees Implementation of the paper titled "Using Sampling to

MIDAS, IIIT Delhi 2 Aug 29, 2022
Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

Min-Max Adversarial Attacks [Paper] [arXiv] [Video] [Slide] Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zha

Jingkang Wang 12 Nov 23, 2022
PyTorch Implementation of Temporal Output Discrepancy for Active Learning, ICCV 2021

Temporal Output Discrepancy for Active Learning PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

Siyu Huang 33 Dec 06, 2022
Code for SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations

The Second Situated Interactive MultiModal Conversations (SIMMC 2.0) Challenge 2021 Welcome to the Second Situated Interactive Multimodal Conversation

Facebook Research 81 Nov 22, 2022
A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

Reinforcement-Learning-Notebooks A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented

Pulkit Khandelwal 1k Dec 28, 2022
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning. In ICCV, 2021.

ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning This repository contains the code for our ICCV 202

sangho.lee 28 Nov 08, 2022
Convert ONNX model graph to Keras model format.

Convert ONNX model graph to Keras model format.

Grigory Malivenko 175 Dec 28, 2022
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Arun 92 Dec 03, 2022
Python Blood Vessel Topology Analysis

Python Blood Vessel Topology Analysis This repository is not being updated anymore. The new version of PyVesTo is called PyVaNe and is available at ht

6 Nov 15, 2022
LowRankModels.jl is a julia package for modeling and fitting generalized low rank models.

LowRankModels.jl LowRankModels.jl is a Julia package for modeling and fitting generalized low rank models (GLRMs). GLRMs model a data array by a low r

Madeleine Udell 183 Dec 17, 2022
Extreme Rotation Estimation using Dense Correlation Volumes

Extreme Rotation Estimation using Dense Correlation Volumes This repository contains a PyTorch implementation of the paper: Extreme Rotation Estimatio

Ruojin Cai 29 Nov 18, 2022
Compare neural networks by their feature similarity

PyTorch Model Compare A tiny package to compare two neural networks in PyTorch. There are many ways to compare two neural networks, but one robust and

Anand Krishnamoorthy 181 Jan 04, 2023
Hyperbolic Hierarchical Clustering.

Hyperbolic Hierarchical Clustering (HypHC) This code is the official PyTorch implementation of the NeurIPS 2020 paper: From Trees to Continuous Embedd

HazyResearch 154 Dec 15, 2022