readme: ---
  language:
  - en
  tags:
  - question-answering
  license: apache-2.0
  datasets:
  - newsqa
  metrics:
  - f1
  - exact_match
  ---
  
  
  # BERT Base Uncased Finetuned on NewsQA
  
  Examples with `noAnswer` and `badQuestion` are not included in the training process.
  
  ```shell
  $ cd ~/projects/transformers/examples/legacy/question-answering
  $ mkdir bert_base_uncased_finetuned_newsqa
  $ python run_newsqa.py \
      --model_type bert \
      --model_name_or_path "bert-base-uncased" \
      --do_train \
      --do_eval \
      --do_lower_case \
      --num_train_epochs 2 \
      --per_gpu_train_batch_size 8 \
      --per_gpu_eval_batch_size 32 \
      --max_seq_length 384 \
      --max_grad_norm inf \
      --doc_stride 128 \
      --train_file "~/projects/data/newsqa/combined-newsqa-data-v1.json" \
      --predict_file "~/projects/data/newsqa/combined-newsqa-data-v1.json" \
      --output_dir "./bert_base_uncased_finetuned_newsqa" \
      --save_steps 20000
  ```
  
  Results:
  
  ```shell
  {'exact': 60.19350380096752, 'f1': 73.29371985128037, 'total': 4341, 'HasAns_exact': 60.19350380096752, 'HasAns_f1': 73.29371985128037, 'HasAns_total': 4341, 'best_exact': 60.19350380096752, 'best_exact_thresh': 0.0, 'best_f1': 73.29371985128037, 'best_f1_thresh': 0.0}
  ```
  
  To prepare the database, follow the instructions on the [NewsQA](https://github.com/Maluuba/newsqa) repository.
  