readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - squad
  model-index:
  - name: run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # run05-roberta-large-squadv1.1-sl384-ds128-e2-tbs16
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset.
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 16
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  
  # Train
  ```bash
  python run_qa.py \
      --model_name_or_path roberta-large \
      --dataset_name squad \
      --do_eval \
      --do_train \
      --evaluation_strategy steps \
      --eval_steps 500 \
      --learning_rate 3e-5 \
      --fp16 \
      --num_train_epochs 2 \
      --per_device_eval_batch_size 64 \
      --per_device_train_batch_size 16 \
      --max_seq_length 384 \
      --doc_stride 128 \
      --save_steps 1000 \
      --logging_steps 1 \
      --overwrite_output_dir \
      --run_name $RUNID \
      --output_dir $OUTDIR
  ```
  
  # Eval
  ```bash
  export CUDA_VISIBLE_DEVICES=0
  
  MODEL=vuiseng9/roberta-l-squadv1.1
  OUTDIR=eval-$(basename $MODEL)
  WORKDIR=transformers/examples/pytorch/question-answering
  cd $WORKDIR
  
  nohup python run_qa.py  \
      --model_name_or_path  $MODEL \
      --dataset_name squad  \
      --do_eval  \
      --per_device_eval_batch_size 16  \
      --max_seq_length 384  \
      --doc_stride 128  \
      --overwrite_output_dir \
      --output_dir $OUTDIR 2>&1 | tee $OUTDIR/run.log &
  ```
  ```bash
    eval_exact_match = 88.4674
    eval_f1          = 94.3001
    eval_samples     =   10790
  ```