<jupyter_start><jupyter_text>Generating Offline augmentations<jupyter_code>labelled_df = pd.read_csv('../data/REVIEWS-clean/in-topic/train.tsv', header=None, 
                          names=['id', 'sentence', 'annotation'], sep='\t')

abstr_sum = nas.AbstSummAug(model_path='t5-base', num_beam=1, max_length=0.5)
samples
[abstr_sum.augment(sample) for sample in samples]
[abstr_sum.augment(sample) for sample in samples]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14855875831485588, 'fraction_numerical': 0.008869179600886918, 'mean_word_length': 5.457142857142857, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '104331', 'license': 'MIT', 'n_tokens_mistral': 171, 'n_tokens_neox': 166, 'n_words': 24, 'path': 'notebooks/02_augmentations.ipynb', 'repo_name': 'Valentyn1997/semi-supervised-text-classification', 'size': '451'}