Automated question generation and question answering from Turkish texts using text-to-text transformers

Overview
citation

If you use this software in your work, please cite as:

@article{akyon2021automated,
  title={Automated question generation and question answering from Turkish texts using text-to-text transformers},
  author={Akyon, Fatih Cagatay and Cavusoglu, Devrim and Cengiz, Cemil and Altinuc, Sinan Onur and Temizel, Alptekin},
  journal={arXiv preprint arXiv:2111.06476},
  year={2021}
}
install
git clone https://github.com/obss/turkish-question-generation.git
cd turkish-question-generation
pip install -r requirements.txt
train
  • start a training using args:
python run.py --model_name_or_path google/mt5-small  --output_dir runs/exp1 --do_train --do_eval --tokenizer_name_or_path mt5_qg_tokenizer --per_device_train_batch_size 4 --gradient_accumulation_steps 2 --learning_rate 1e-4 --seed 42 --save_total_limit 1
python run.py config.json
python run.py config.yaml
evaluate
  • arrange related params in config:
do_train: false
do_eval: true
eval_dataset_list: ["tquad2-valid", "xquad.tr"]
prepare_data: true
mt5_task_list: ["qa", "qg", "ans_ext"]
mt5_qg_format: "both"
no_cuda: false
  • start an evaluation:
python run.py config.yaml
neptune
  • install neptune:
pip install neptune-client
  • download config file and arrange neptune params:
run_name: 'exp1'
neptune_project: 'name/project'
neptune_api_token: 'YOUR_API_TOKEN'
  • start a training:
python train.py config.yaml
wandb
  • install wandb:
pip install wandb
  • download config file and arrange wandb params:
run_name: 'exp1'
wandb_project: 'turque'
  • start a training:
python train.py config.yaml
finetuned checkpoints
Name Model data
train
params
(M)
model size
(GB)
turque-s1 mt5-small tquad2-train+tquad2-valid+xquad.tr 60M 1.2GB
mt5-small-3task-both-tquad2 mt5-small tquad2-train 60M 1.2GB
mt5-small-3task-prepend-tquad2 mt5-small tquad2-train 60M 1.2GB
mt5-base-3task-both-tquad2 mt5-base tquad2-train 220M 2.3GB
format
  • answer extraction:

input:

Osman Bey 1258 yılında Söğüt’te doğdu. Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi." ">
"
      
        Osman Bey 1258 yılında Söğüt’te doğdu. 
       
         Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi."

       
      

target:


    
      1258 
     
       Söğüt’te 
      

      
     
    
  • question answering:

input:

"question: Osman Bey nerede doğmuştur? context: Osman Bey 1258 yılında Söğüt’te doğdu. Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi."

target:

"Söğüt’te"
  • question generation (prepend):

input:

"answer: Söğüt’te context: Osman Bey 1258 yılında Söğüt’te doğdu. Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi."

target:

"Osman Bey nerede doğmuştur?"
  • question generation (highlight):

input:

Söğüt’te doğdu. Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi." ">
"generate question: Osman Bey 1258 yılında 
     
       Söğüt’te 
      
        doğdu. Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi."

      
     

target:

"Osman Bey nerede doğmuştur?"
  • question generation (both):

input:

Söğüt’te doğdu. Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi." ">
"answer: Söğüt’te context: Osman Bey 1258 yılında 
     
       Söğüt’te 
      
        doğdu. Osman Bey 1 Ağustos 1326’da Bursa’da hayatını kaybetmiştir.1281 yılında Osman Bey 23 yaşında iken Ahi teşkilatından olan Şeyh Edebali’nin kızı Malhun Hatun ile evlendi."

      
     

target:

"Osman Bey nerede doğmuştur?"
paper results
BERTurk-base and mT5-base QA evaluation results for TQuADv2 fine-tuning.

mT5-base QG evaluation results for single-task (ST) and multi-task (MT) for TQuADv2 fine-tuning.

TQuADv1 and TQuADv2 fine-tuning QG evaluation results for multi-task mT5 variants. MT-Both means, mT5 model is fine-tuned with ’Both’ input format and in a multi-task setting.

paper configs

You can find the config files used in the paper under configs/paper.

contributing

Before opening a PR:

  • Install required development packages:
pip install "black==21.7b0" "flake8==3.9.2" "isort==5.9.2"
  • Reformat with black and isort:
black . --config pyproject.toml
isort .
You might also like...
NeuralQA: A Usable Library for Question Answering on Large Datasets with BERT
NeuralQA: A Usable Library for Question Answering on Large Datasets with BERT

NeuralQA: A Usable Library for (Extractive) Question Answering on Large Datasets with BERT Still in alpha, lots of changes anticipated. View demo on n

Knowledge Graph,Question Answering System,基于知识图谱和向量检索的医疗诊断问答系统
Knowledge Graph,Question Answering System,基于知识图谱和向量检索的医疗诊断问答系统

Knowledge Graph,Question Answering System,基于知识图谱和向量检索的医疗诊断问答系统

Baseline code for Korean open domain question answering(ODQA)
Baseline code for Korean open domain question answering(ODQA)

Open-Domain Question Answering(ODQA)는 다양한 주제에 대한 문서 집합으로부터 자연어 질의에 대한 답변을 찾아오는 task입니다. 이때 사용자 질의에 답변하기 위해 주어지는 지문이 따로 존재하지 않습니다. 따라서 사전에 구축되어있는 Knowl

Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering

Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018) dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors.

CCQA A New Web-Scale Question Answering Dataset for Model Pre-Training

CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training This is the official repository for the code and models of the paper CCQA: A N

chaii - hindi & tamil question answering

chaii - hindi & tamil question answering This is the solution for rank 5th in Kaggle competition: chaii - Hindi and Tamil Question Answering. The comp

Contact Extraction with Question Answering.

contactsQA Extraction of contact entities from address blocks and imprints with Extractive Question Answering. Goal Input: Dr. Max Mustermann Hauptstr

BERT-based Financial Question Answering System
BERT-based Financial Question Answering System

BERT-based Financial Question Answering System In this example, we use Jina, PyTorch, and Hugging Face transformers to build a production-ready BERT-b

Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts
Python package to easily retrain OpenAI's GPT-2 text-generating model on new texts

gpt-2-simple A simple Python package that wraps existing model fine-tuning and generation scripts for OpenAI's GPT-2 text generation model (specifical

Owner
Open Business Software Solutions
Open Source for Open Business
Open Business Software Solutions
Code for Findings at EMNLP 2021 paper: "Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning"

Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning This repo is for Findings at EMNLP 2021 paper: Learn Cont

INK Lab @ USC 6 Sep 02, 2022
Py65 65816 - Add support for the 65C816 to py65

Add support for the 65C816 to py65 Py65 (https://github.com/mnaberez/py65) is a

4 Jan 04, 2023
FedNLP: A Benchmarking Framework for Federated Learning in Natural Language Processing

FedNLP is a research-oriented benchmarking framework for advancing federated learning (FL) in natural language processing (NLP). It uses FedML repository as the git submodule. In other words, FedNLP

FedML-AI 216 Nov 27, 2022
This repository collects together basic linguistic processing data for using dataset dumps from the Common Voice project

Common Voice Utils This repository collects together basic linguistic processing data for using dataset dumps from the Common Voice project. It aims t

Francis Tyers 40 Dec 20, 2022
Signature remover is a NLP based solution which removes email signatures from the rest of the text.

Signature Remover Signature remover is a NLP based solution which removes email signatures from the rest of the text. It helps to enchance data conten

Forges Alterway 8 Jan 06, 2023
Synthetic data for the people.

zpy: Synthetic data in Blender. Website • Install • Docs • Examples • CLI • Contribute • Licence Abstract Collecting, labeling, and cleaning data for

Zumo Labs 253 Dec 21, 2022
iBOT: Image BERT Pre-Training with Online Tokenizer

Image BERT Pre-Training with iBOT Official PyTorch implementation and pretrained models for paper iBOT: Image BERT Pre-Training with Online Tokenizer.

Bytedance Inc. 435 Jan 06, 2023
:hot_pepper: R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)

R²SQL The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021) Requirement

huybery 60 Dec 31, 2022
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries

Moment-DETR QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries Jie Lei, Tamara L. Berg, Mohit Bansal For dataset de

Jie Lei 雷杰 133 Dec 22, 2022
ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体

ChainKnowledgeGraph, 产业链知识图谱包括A股上市公司、行业和产品共3类实体,包括上市公司所属行业关系、行业上级关系、产品上游原材料关系、产品下游产品关系、公司主营产品、产品小类共6大类。 上市公司4,654家,行业511个,产品95,559条、上游材料56,824条,上级行业480条,下游产品390条,产品小类52,937条,所属行业3,946条。

liuhuanyong 415 Jan 06, 2023
Twitter bot that uses NLP models to summarize news articles referenced in a user's twitter timeline

Twitter-News-Summarizer Twitter bot that uses NLP models to summarize news articles referenced in a user's twitter timeline 1.) Extracts all tweets fr

Rohit Govindan 1 Jan 27, 2022
Training and evaluation codes for the BertGen paper (ACL-IJCNLP 2021)

BERTGEN This repository is the implementation of the paper "BERTGEN: Multi-task Generation through BERT" (https://arxiv.org/abs/2106.03484). The codeb

<a href=[email protected]"> 9 Oct 26, 2022
NLP - Machine learning

Flipkart-product-reviews NLP - Machine learning About Product reviews is an essential part of an online store like Flipkart’s branding and marketing.

Harshith VH 1 Oct 29, 2021
Rank-One Model Editing for Locating and Editing Factual Knowledge in GPT

Rank-One Model Editing (ROME) This repository provides an implementation of Rank-One Model Editing (ROME) on auto-regressive transformers (GPU-only).

Kevin Meng 130 Dec 21, 2022
I can help you convert your images to pdf file.

IMAGE TO PDF CONVERTER BOT Configs TOKEN - Get bot token from @BotFather API_ID - From my.telegram.org API_HASH - From my.telegram.org Deploy to Herok

MADUSHANKA 10 Dec 14, 2022
End-2-end speech synthesis with recurrent neural networks

Introduction New: Interactive demo using Google Colaboratory can be found here TTS-Cube is an end-2-end speech synthesis system that provides a full p

Tiberiu Boros 214 Dec 07, 2022
NeurIPS'21: Probabilistic Margins for Instance Reweighting in Adversarial Training (Pytorch implementation).

source code for NeurIPS21 paper robabilistic Margins for Instance Reweighting in Adversarial Training

9 Dec 20, 2022
Local cross-platform machine translation GUI, based on CTranslate2

DesktopTranslator Local cross-platform machine translation GUI, based on CTranslate2 Download Windows Installer You can either download a ready-made W

Yasmin Moslem 29 Jan 05, 2023
Persian Bert For Long-Range Sequences

ParsBigBird: Persian Bert For Long-Range Sequences The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many ta

Sajjad Ayoubi 63 Dec 14, 2022
In this Notebook I've build some machine-learning and deep-learning to classify corona virus tweets, in both multi class classification and binary classification.

Hello, This Notebook Contains Example of Corona Virus Tweets Multi Class Classification. - Classes is: Extremely Positive, Positive, Extremely Negativ

Khaled Tofailieh 3 Dec 06, 2022