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Technical Question Answering across Tasks and Domains, NAACL 2021
Are the hyperparameters you have in the README what you used to produce the checkpoint-1 & 2 you shared (with different seeds)?
python run_techqa.py \
--model_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking-finetuned-squad \
--do_lower_case \
--num_train_epochs 20 \
--learning_rate 5.5e-6 \
--do_train \
--train_file <PATH TO training_Q_A.json> \
--do_eval \
--predict_file <PATH TO dev_Q_A.json> \
--input_corpus_file <PATH TO training_dev_technotes.json> \
--per_gpu_train_batch_size 4 \
--predict_batch_size 16 \
--overwrite_output_dir \
--output_dir <PATH TO OUTPUT FOLDER> \
--add_doc_title_to_passage \
In terms of training procedure, based on what described in the paper, should someone use this to train on Squad dataset from bert-large-uncased-whole-word-masking-finetuned-squad
first, then train on TechQA to reach those checkpoint accuracy performance?
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