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Add argparse parametrization for the finetuning script #7

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gsarti opened this issue Mar 25, 2020 · 0 comments

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enhancement good first issue help wanted

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gsarti commented Mar 25, 2020 •

edited

Similar to what is currently available in download_model.py, add Argparse with parameters in finetune_nli.py for parameters:

model_name, default 'models/scibert', type str
batch_size, default 64, type int
model_save_path, default 'models/scibert_nli', type str
num_epochs, default 2, type int
warmup_steps, default None, not required
do_mean_pooling with action='store_true'
do_cls_pooling with action='store_true'
do_max_pooling with action='store_true'

Then:

Add check for only one of the pooling condition to be verified (raise an AttributeError if more than one is). If none is specified, we use mean pooling strategy.
Check if the warmup_step parameter is set before setting it to 10% of training: if it is, keep the user-defined value.

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gsarti added enhancement help wanted good first issue labels

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