代码结果不好 about quasi-attention-absa HOT 3 OPEN

i just run the run.sh file following

# example running command
CUDA_VISIBLE_DEVICES=0 python run_classifier.py \
--task_name semeval_NLI_M \
--data_dir ../datasets/semeval2014/ \
--output_dir ../results/semeval2014/QACGBERT-2/ \
--model_type QACGBERT \
--do_lower_case \
--max_seq_length 128 \
--train_batch_size 24 \
--eval_batch_size 24 \
--learning_rate 2e-5 \
--num_train_epochs 30 \
--vocab_file ../models/BERT-Google/vocab.txt \
--bert_config_file ../models/BERT-Google/bert_config.json \
--init_checkpoint ../models/BERT-Google/pytorch_model.bin \
--seed 123 \
--evaluate_interval 250 \
--context_standalone

from quasi-attention-absa.

Hi,

Thanks for your feedback. Could you provide your running command so I can further root cause the issue?

Thanks.

from quasi-attention-absa.

Thanks! I am sorry that I did not keep this repo updated in the first place. Here is the reason why you experience this catastrophic failure.

I updated this repo for other projects by studying different training rates scheduling for different layers, which is not a topic for this paper. And that causes some issues (it seems like from your runs!) If you look at my recent push, I commented out those lines for the PR opened by you:
770d810
Without this change, it seems like I was trying out some really high learning rate for some linear layers, and that failed the training.

You can do the following things to remediate the catastrophic failure:
(1) pull.
(2) rerun with the updated commands as well.

Since I was working on this repo for other projects, I might forget to remove codes here and there. When I have time, I will update it all at once. Thanks again for your findings! It matters! If you still experience this catastrophic failure, please let me know. If not, please kindly close this issue.

Thanks,
Zen

from quasi-attention-absa.