A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

Overview

DrQA

A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA).

Reading comprehension is a task to produce an answer when given a question and one or more pieces of evidence (usually natural language paragraphs). Compared to question answering over knowledge bases, reading comprehension models are more flexible and have revealed a great potential for zero-shot learning.

SQuAD is a reading comprehension benchmark where there's only a single piece of evidence and the answer is guaranteed to be a part of the evidence. Since the publication of SQuAD dataset, there has been fast progress in the research of reading comprehension and a bunch of great models have come out. DrQA is one that is conceptually simpler than most others but still yields strong performance even as a single model.

The motivation for this project is to offer a clean version of DrQA for the machine reading comprehension task, so one can quickly do some modifications and try out new ideas. Click here to see the comparison with what's described in the original paper and with two "official" projects ParlAI and DrQA.

Requirements

Quick Start

Setup

  • download the project via git clone https://github.com/hitvoice/DrQA.git; cd DrQA
  • make sure python 3, pip, wget and unzip are installed.
  • install pytorch matched with your OS, python and cuda versions.
  • install the remaining requirements via pip install -r requirements.txt
  • download the SQuAD datafile, GloVe word vectors and Spacy English language models using bash download.sh.

Train

# prepare the data
python prepro.py
# train for 40 epochs with batchsize 32
python train.py -e 40 -bs 32

Warning: Running prepro.py takes about 9G memory when using 8 threads. If there's not enough memory on your machine, try reducing the number of threads used by the script, for example, python prepro.py --threads 2

Predict

python interact.py

Example interactions:

Evidence: Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24-10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.
Question: What day was the game played on?
Answer: February 7, 2016
Time: 0.0245s

Evidence: Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24-10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.
Question: What is the AFC short for?
Answer: The American Football Conference
Time: 0.0214s

Evidence: Beanie style with simple design. So cool to wear and make you different. It wears as peak cap and a fashion cap. It is good to match your clothes well during party and holiday, also makes you charming and fashion, leisure and fashion in public and streets. It suits all adults, for men or women. Matches well with your winter outfits so you stay warm all winter long.
Question: Is it for women?
Answer: It suits all adults, for men or women
Time: 0.0238s

The last example is a randomly picked product description from Amazon (not in SQuAD).

Results

EM & F1

EM F1
in the original paper 69.5 78.8
in this project 69.64 78.76
offical(Spacy) 69.71 78.94
offical(CoreNLP) 69.76 79.09

Compared with the official implementation:

Detailed Comparisons

Compared to what's described in the original paper:

  • The grammatical features are generated by spaCy instead of Stanford CoreNLP. It's much faster and produces similar scores.

Compared to the code in facebookresearch/DrQA:

  • This project is much more light-weighted and focusing solely on training and evaluating on SQuAD dataset while lacking the document retriever, the interactive inference API, and some other features.
  • The implementation in facebookresearch/DrQA is able to train on multiple GPUs, while (currently and for simplicity) in this implementation we only support single-GPU training.

Compared to the code in facebookresearch/ParlAI:

  • The DrQA model is no longer wrapped in a chatbot framework, which makes the code more readable, easier to modify and is faster to train. The preprocessing for text corpus is performed only once, while in a dialog framework raw text is transmitted each time and preprocessing for the same text must be done again and again.
  • This is a full implementation of the original paper, while the model in ParlAI is a partial implementation, missing all grammatical features (lemma, POS tags and named entity tags).
  • Some minor bug fixes. Some of them have been merged into ParlAI.

About

Maintainer: Runqi Yang.

Credits: thank Jun Yang for code review and advice.

Most of the pytorch model code is borrowed from Facebook/ParlAI under a BSD-3 license.

Owner
Runqi Yang
ML engineer @Alibaba. Interested in conversational systems and deep learning.
Runqi Yang
git《Beta R-CNN: Looking into Pedestrian Detection from Another Perspective》(NeurIPS 2020) GitHub:[fig3]

Beta R-CNN: Looking into Pedestrian Detection from Another Perspective This is the pytorch implementation of our paper "[Beta R-CNN: Looking into Pede

35 Sep 08, 2021
Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Equinox Callable PyTrees and filtered JIT/grad transformations = neural networks in JAX Equinox brings more power to your model building in JAX. Repr

Patrick Kidger 909 Dec 30, 2022
Codebase for "Revisiting spatio-temporal layouts for compositional action recognition" (Oral at BMVC 2021).

Revisiting spatio-temporal layouts for compositional action recognition Codebase for "Revisiting spatio-temporal layouts for compositional action reco

Gorjan 20 Dec 15, 2022
Implementation of CVPR'21: RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction

RfD-Net [Project Page] [Paper] [Video] RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction Yinyu Nie, Ji Hou, Xiaoguang Han, Matthi

Yinyu Nie 162 Jan 06, 2023
Discovering and Achieving Goals via World Models

Discovering and Achieving Goals via World Models [Project Website] [Benchmark Code] [Video (2min)] [Oral Talk (13min)] [Paper] Russell Mendonca*1, Ole

Oleg Rybkin 71 Dec 22, 2022
Implementation of various Vision Transformers I found interesting

Implementation of various Vision Transformers I found interesting

Kim Seonghyeon 78 Dec 06, 2022
Official implementation of the paper "Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering"

Light Field Networks Project Page | Paper | Data | Pretrained Models Vincent Sitzmann*, Semon Rezchikov*, William Freeman, Joshua Tenenbaum, Frédo Dur

Vincent Sitzmann 130 Dec 29, 2022
FastReID is a research platform that implements state-of-the-art re-identification algorithms.

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

JDAI-CV 2.8k Jan 07, 2023
Everything about being a TA for ITP/AP course!

تی‌ای بودن! تی‌ای یا دستیار استاد از نقش‌های رایج بین دانشجویان مهندسی است، این ریپوزیتوری قرار است نکات مهم درمورد تی‌ای بودن و تی ای شدن را به ما نش

<a href=[email protected]"> 14 Sep 10, 2022
Official Pytorch implementation of C3-GAN

Official pytorch implemenation of C3-GAN Contrastive Fine-grained Class Clustering via Generative Adversarial Networks [Paper] Authors: Yunji Kim, Jun

NAVER AI 114 Dec 02, 2022
RADIal is available now! Check the download section

Latest news: RADIal is available now! Check the download section. However, because we are currently working on the data anonymization, we provide for

valeo.ai 55 Jan 03, 2023
MazeRL is an application oriented Deep Reinforcement Learning (RL) framework

MazeRL is an application oriented Deep Reinforcement Learning (RL) framework, addressing real-world decision problems. Our vision is to cover the complete development life cycle of RL applications ra

EnliteAI GmbH 222 Dec 24, 2022
Categorizing comments on YouTube into different categories.

Youtube Comments Categorization This repo is for categorizing comments on a youtube video into different categories. negative (grievances, complaints,

Rhitik 5 Nov 26, 2022
Few-Shot-Intent-Detection includes popular challenging intent detection datasets with/without OOS queries and state-of-the-art baselines and results.

Few-Shot-Intent-Detection Few-Shot-Intent-Detection is a repository designed for few-shot intent detection with/without Out-of-Scope (OOS) intents. It

Jian-Guo Zhang 73 Dec 26, 2022
Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

CLIPstyler Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition" Environment Pytorch 1.7.1, Python 3.6 $ c

203 Dec 30, 2022
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation Figure 1: We estimate the 6DoF rigid transformation of a 3D face (rendered in si

Vítor Albiero 519 Dec 29, 2022
Management Dashboard for Torchserve

Torchserve Dashboard Torchserve Dashboard using Streamlit Related blog post Usage Additional Requirement: torchserve (recommended:v0.5.2) Simply run:

Ceyda Cinarel 103 Dec 10, 2022
Code for Referring Image Segmentation via Cross-Modal Progressive Comprehension, CVPR2020.

CMPC-Refseg Code of our CVPR 2020 paper Referring Image Segmentation via Cross-Modal Progressive Comprehension. Shaofei Huang*, Tianrui Hui*, Si Liu,

spyflying 55 Dec 01, 2022
Ludwig Benchmarking Toolkit

Ludwig Benchmarking Toolkit The Ludwig Benchmarking Toolkit is a personalized benchmarking toolkit for running end-to-end benchmark studies across an

HazyResearch 17 Nov 18, 2022
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision

Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision Project | PDF | Poster Fangyu Li, N. Dinesh Reddy, X

25 Dec 21, 2022