Catbird is an open source paraphrase generation toolkit based on PyTorch.

Overview

License: MIT

Catbird is an open source paraphrase generation toolkit based on PyTorch.

Quick Start

Requirements and Installation

The project is based on PyTorch 1.5+ and Python 3.6+.

Install Catbird

The package can be installed using pip:

pip install catbird

This does not include configuration files or tools. Alternatively, you can run from the source code:

a. Clone the repository.

git clone https://github.com/AfonsoSalgadoSousa/catbird.git

b. Install dependencies. This project uses Poetry as its package manager. There should Make sure you have it installed. For more info check Poetry's official documentation. To install dependencies, simply run:

poetry install

Dataset Preparation

For now, we only work with the Quora Question Pairs dataset. It is recommended to download and extract the datasets somewhere outside the project directory and symlink the dataset root to $CATBIRD/data as below. If your folder structure is different, you may need to change the corresponding paths in config files.

catbird
├── catbird
├── tools
├── configs
├── data
│   ├── quora
│   │   ├── quora_duplicate_questions.tsv

We use the HuggingFace Datasets library to load the datasets.

Train

poetry run python tools/train.py ${CONFIG_FILE} [optional arguments]

Example:

  1. Train T5 on QQP.
$ python tools/train.py configs/t5_quora.yaml

Contributors

Owner
Afonso Salgado de Sousa
Afonso Salgado de Sousa
Pre-Training 3D Point Cloud Transformers with Masked Point Modeling

Point-BERT: Pre-Training 3D Point Cloud Transformers with Masked Point Modeling Created by Xumin Yu*, Lulu Tang*, Yongming Rao*, Tiejun Huang, Jie Zho

Lulu Tang 306 Jan 06, 2023
Anagram Generator in Python

Anagrams Generator This is a program for computing multiword anagrams. It makes no effort to come up with sentences that make sense; it only finds ana

Day Fundora 5 Nov 17, 2022
Cleaned up code for DSTC 10: SIMMC 2.0 track: subtask 2: multimodal coreference resolution

UNITER-Based Situated Coreference Resolution with Rich Multimodal Input: arXiv MMCoref_cleaned Code for the MMCoref task of the SIMMC 2.0 dataset. Pre

Yichen (William) Huang 2 Dec 05, 2022
A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).

LegoNet This code is the implementation of ICML2019 paper LegoNet: Efficient Convolutional Neural Networks with Lego Filters Run python train.py You c

YangZhaohui 140 Sep 26, 2022
Pure python PEMDAS expression solver without using built-in eval function

pypemdas Pure python PEMDAS expression solver without using built-in eval function. Supports nested parenthesis. Supported operators: + - * / ^ Exampl

1 Dec 22, 2021
Code repo for "Transformer on a Diet" paper

Transformer on a Diet Reference: C Wang, Z Ye, A Zhang, Z Zhang, A Smola. "Transformer on a Diet". arXiv preprint arXiv (2020). Installation pip insta

cgraywang 31 Sep 26, 2021
Official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting

1 SNAS4MTF This repo is the official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting. 1.1 The frame

SZJ 5 Sep 21, 2022
This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems.

Amortized Assimilation This repository contains a PyTorch implementation of the paper Learning to Assimilate in Chaotic Dynamical Systems. Abstract: T

4 Aug 16, 2022
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Automated Side Channel Analysis of Media Software with Manifold Learning Official implementation of USENIX Security 2022 paper: Automated Side Channel

Yuanyuan Yuan 175 Jan 07, 2023
Jupyter notebooks for using & learning Keras

deep-learning-with-keras-notebooks 這個github的repository主要是個人在學習Keras的一些記錄及練習。希望在學習過程中發現到一些好的資訊與範例也可以對想要學習使用 Keras來解決問題的同好,或是對深度學習有興趣的在學學生可以有一些方便理解與上手範例

ErhWen Kuo 2.1k Dec 27, 2022
Code for Environment Dynamics Decomposition (ED2).

ED2 Code for Environment Dynamics Decomposition (ED2). Installation Follow the installation in MBPO and Dreamer. Usage First follow the SD2 method for

0 Aug 10, 2021
PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning"

PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".

Berivan Isik 8 Dec 08, 2022
Machine Learning Models were applied to predict the mass of the brain based on gender, age ranges, and head size.

Brain Weight in Humans Variations of head sizes and brain weights in humans Kaggle dataset obtained from this link by Anubhab Swain. Image obtained fr

Anne Livia 1 Feb 02, 2022
Simulation of Self Driving Car

In this repository, the code to use Udacity's self driving car simulator as a testbed for training an autonomous car are provided.

Shyam Das Shrestha 1 Nov 21, 2021
MoCap-Solver: A Neural Solver for Optical Motion Capture Data

MoCap-Solver is a data-driven-based robust marker denoising method, which takes raw mocap markers as input and outputs corresponding clean markers and skeleton motions.

55 Dec 28, 2022
Face2webtoon - Despite its importance, there are few previous works applying I2I translation to webtoon.

Despite its importance, there are few previous works applying I2I translation to webtoon. I collected dataset from naver webtoon 연애혁명 and tried to transfer human faces to webtoon domain.

이상윤 64 Oct 19, 2022
Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

ProbAI 2021 - Probabilistic Programming and Variational Inference Tutorial with Pryo Day 1 (June 14) Slides Notebook: students_PPLs_Intro Notebook: so

PGM-Lab 46 Nov 01, 2022
Evaluating Cross-lingual Sentence Representations

XNLI: The Cross-Lingual NLI Corpus XNLI is an evaluation corpus for language transfer and cross-lingual sentence classification in 15 languages. New:

Meta Research 395 Dec 19, 2022
source code for 'Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge' by A. Shah, K. Shanmugam, K. Ahuja

Source code for "Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge" Reference: Abhin Shah, Karthikeyan Shanmugam, Kartik Ahu

Abhin Shah 1 Jun 03, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Dec 28, 2022