fix import issue when running huggingface_lowresource.sh#4
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RenShuhuai-Andy
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Nov 13, 2021
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Thanks very much for the PR, here is some feedback:
- There is no need to change the way of importing packages in taa/archive.py etc. since I have moved the
__main__function in thetaa/search.pyoutside (examples/reproduce_experiment.py). - The
__main__function intaa/search_augment_train.pymay also need to move to theexamplesfold, please check it. - If a dataset in
huggingface/datasetsdoesn't have the original val set, please split out 10% training samples for validation. - Other specific suggestions on revision are commented after each file, please check it.
Thanks again.
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| if C.get()['ir'] < 1 and C.get()['method'] != 'bt': | ||
| # rebalanced data | ||
| ir_index = np.where(labels == 0) | ||
| texts = np.append(texts, texts[ir_index].repeat(int(1 / C.get()['ir']) - 1)) | ||
| labels = np.append(labels, labels[ir_index].repeat(int(1 / C.get()['ir']) - 1)) |
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| transform_train.transforms.insert(0, Augmentation(default_policy())) | ||
| pass |
| ('random_word_swap', 0.4009335761117499, 0.3015697007069029)]] | ||
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| def default_policy(): |
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I have deleted the default policy since it is not been used anymore
| class_num = train_dataset.features['label'].num_classes | ||
| all_train_examples = get_examples(train_dataset, text_key) | ||
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| train_examples, valid_examples = general_split(all_train_examples, test_size=test_size, train_size=1-test_size) |
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A judgment statement should be added: if the dataset originally has a validation set, there is no need to split the val set from the training set.
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