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
Proquabet - Convert your prose into proquints and then you essentially have Vogon poetry

Proquabet Turn your prose into a constant stream of encrypted and meaningless-so

Milo Fultz 2 Oct 10, 2022
ConvBERT: Improving BERT with Span-based Dynamic Convolution

ConvBERT Introduction In this repo, we introduce a new architecture ConvBERT for pre-training based language model. The code is tested on a V100 GPU.

YITUTech 237 Dec 10, 2022
A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"

A sample Python project A sample project that exists as an aid to the Python Packaging User Guide's Tutorial on Packaging and Distributing Projects. T

Python Packaging Authority 4.5k Dec 30, 2022
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 15.3k Jan 03, 2023
American Sign Language (ASL) to Text Converter

Signterpreter American Sign Language (ASL) to Text Converter Recommendations Although there is grayscale and gaussian blur, we recommend that you use

0 Feb 20, 2022
Examples of using sparse attention, as in "Generating Long Sequences with Sparse Transformers"

Status: Archive (code is provided as-is, no updates expected) Update August 2020: For an example repository that achieves state-of-the-art modeling pe

OpenAI 1.3k Dec 28, 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
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation

SHAS: Approaching optimal Segmentation for End-to-End Speech Translation In this repo you can find the code of the Supervised Hybrid Audio Segmentatio

Machine Translation @ UPC 21 Dec 20, 2022
Codes to pre-train Japanese T5 models

t5-japanese Codes to pre-train a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts. The model is available at https://hug

Megagon Labs 37 Dec 25, 2022
Translate U is capable of translating the text present in an image from one language to the other.

Translate U is capable of translating the text present in an image from one language to the other. The app uses OCR and Google translate to identify and translate across 80+ languages.

Neelanjan Manna 1 Dec 22, 2021
🕹 An esoteric language designed so that the program looks like the transcript of a Pokémon battle

PokéBattle is an esoteric language designed so that the program looks like the transcript of a Pokémon battle. Original inspiration and specification

Eduardo Correia 9 Jan 11, 2022
Python functions for summarizing and improving voice dictation input.

Helpmespeak Help me speak uses Python functions for summarizing and improving voice dictation input. Get started with OpenAI gpt-3 OpenAI is a amazing

Margarita Humanitarian Foundation 6 Dec 17, 2022
Transformation spoken text to written text

Transformation spoken text to written text This model is used for formatting raw asr text output from spoken text to written text (Eg. date, number, i

Nguyen Binh 16 Dec 28, 2022
p-tuning for few-shot NLU task

p-tuning_NLU Overview 这个小项目是受乐于分享的苏剑林大佬这篇p-tuning 文章启发,也实现了个使用P-tuning进行NLU分类的任务, 思路是一样的,prompt实现方式有不同,这里是将[unused*]的embeddings参数抽取出用于初始化prompt_embed后

3 Dec 29, 2022
A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP

A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP

420 Dec 28, 2022
VoiceFixer VoiceFixer is a framework for general speech restoration.

VoiceFixer VoiceFixer is a framework for general speech restoration. We aim at the restoration of severly degraded speech and historical speech. Paper

Leo 174 Jan 06, 2023
Image2pcl - Enter the metaverse with 2D image to 3D projections

Image2PCL Enter the metaverse with 2D image to 3D projections! This is an implem

Benjamin Ho 0 Feb 05, 2022
Almost State-of-the-art Text Generation library

Ps: we are adding transformer model soon Text Gen 🐐 Almost State-of-the-art Text Generation library Text gen is a python library that allow you build

Emeka boris ama 63 Jun 24, 2022
OceanScript is an Esoteric language used to encode and decode text into a formulation of characters

OceanScript is an Esoteric language used to encode and decode text into a formulation of characters - where the final result looks like waves in the ocean.

Creating a python chatbot that Starbucks users can text to place an order + help cut wait time of a normal coffee.

Creating a python chatbot that Starbucks users can text to place an order + help cut wait time of a normal coffee.

2 Jan 20, 2022