Comments (1)
Sorry for the late reply. I just checked my experiment logs. It takes roughly 1200 seconds for training with large
-series PLM per epoch, with running script in run/run_train_lgesql_plm.sh
. Sadly, it is a little slow in your experiment. But, 200 epochs is not a necessity. Actually, 100 epochs is enough for comparable performances. We trained the large-series PLM for more epochs just for more stable performances according to this work.
If you just want to verify your ideas, why not experiment with Glove
embeddings or base
-series PLM? It can be much faster. We did not spend too much time on hyper-parameter tuning with large-series PLM and most ablation studies are conducted with Glove
embeddings. As for how to set the grad_accumulate
hyper-parameter (the actual mini-batch size for each forward process is batch_size/grad_accumulate
), you can run and check the usage of CUDA memory based on your GPU device.
Attention: there is a mistake about learning rate in the original paper. Learning rate should be 1e-4 for large-series PLM and 2e-4 for base-series PLM (not e-5
).
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Related Issues (20)
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