readme: 
  # Turkish QNLI Model
  
  I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD 
  https://huggingface.co/dbmdz/bert-base-turkish-uncased
  
  # Data: TQuAD
  I used following TQuAD data set
  
  https://github.com/TQuad/turkish-nlp-qa-dataset
  
  I convert the dataset into transformers glue data format of QNLI by the following script
  SQuAD -> QNLI
  
  ```
  import argparse
  import collections
  import json
  import numpy as np
  import os
  import re
  import string
  import sys
  
  ff="dev-v0.1.json"
  ff="train-v0.1.json"
  dataset=json.load(open(ff))
  
  i=0
  for article in dataset['data']:
   title= article['title']
   for p in article['paragraphs']:
    context= p['context']
    for qa in p['qas']:
     answer= qa['answers'][0]['text']
     all_other_answers= list(set([e['answers'][0]['text'] for e in p['qas']]))
     all_other_answers.remove(answer)
     i=i+1
     print(i,qa['question'].replace(";",":") , answer.replace(";",":"),"entailment", sep="\t")
     for other in all_other_answers:
      i=i+1
      print(i,qa['question'].replace(";",":") , other.replace(";",":"),"not_entailment" ,sep="\t")
    
  ```
  
  
  Under QNLI folder there are dev and test test
  Training data looks like 
  > 613     II.Friedrich’in bilginler arasındaki en önemli şahsiyet olarak belirttiği kişi kimdir?  filozof, kimyacı, astrolog ve çevirmen  not_entailment
  > 614     II.Friedrich’in bilginler arasındaki en önemli şahsiyet olarak belirttiği kişi kimdir?  kişisel eğilimi ve özel temaslar nedeniyle      not_entailment
  > 615     Michael Scotus’un mesleği nedir?        filozof, kimyacı, astrolog ve çevirmen  entailment
  > 616     Michael Scotus’un mesleği nedir?        Palermo’ya      not_entailment
  
  
  
  
  
  # Training
  
  Training the model with following environment
  ```
  export GLUE_DIR=./glue/glue_dataTR/QNLI
  export TASK_NAME=QNLI
  ```
  
  ```
  python3 run_glue.py \
    --model_type bert \
    --model_name_or_path dbmdz/bert-base-turkish-uncased\
    --task_name $TASK_NAME \
    --do_train \
    --do_eval \
    --data_dir $GLUE_DIR \
    --max_seq_length 128 \
    --per_gpu_train_batch_size 32 \
    --learning_rate 2e-5 \
    --num_train_epochs 3.0 \
    --output_dir /tmp/$TASK_NAME/
  
  ```
  
  
  # Evaluation Results
  
  ==
  | acc | 0.9124060613527165
  | loss| 0.21582801340189717
  ==
  
  > See all my model
  > https://huggingface.co/savasy
  
  
  
  
  
  
  