Hi,
I am working on fine-tuning different transformers models for question answering in aerospace engineering domain.
I have selected BERT as my baseline model which I am fine-tuning on SQuAD 2.0 using run_qa.py. For testing, I have created my own test dataset that comprises of questions related to scientific papers in NASA Technical Reports Server.
As the baseline, I use the parameters below,
python3 run_qa.py \
    --model_name_or_path bert-base-uncased \
    --dataset_name squad_v2 \
    --do_train \
    --do_eval \
    --version_2_with_negative \
    --num_train_epochs 3 \
    --learning_rate 3e-5 \
    --max_seq_length 384 \
    --doc_stride 128 \
    --per_device_train_batch_size 16 \
    --gradient_accumulation_steps 1 \
    --per_device_eval_batch_size 16 \
    --logging_steps 50 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --output_dir trials/bert_base_lr_3e_5

If I only change max_seq_length from 384 to 448, then on the 2nd epoch, validation F1 and EM scores increase. However, on my test set, accuracies drop considerably per the table below. I am wondering if there is a logical explanation as to why the accuracy is dropping in my test set despite the increase in validation scores? As far as I understand, increasing the maximum sequence length could increase the model prediction accuracy because the model can take longer dependencies into account.




max_seq_length
Epoch
Test F1
Test EM
Eval F1
Eval EM




384
3
63.16
57
76.72
73.33


448
2
57.65
54
77.32
74.29