<jupyter_start><jupyter_text>missings<jupyter_code>all_df.count()
all_df.shape
all_df['label'].unique()<jupyter_output><empty_output><jupyter_text>Target Rate<jupyter_code>all_df['label'].mean(), train_df['label'].mean(), dev_df['label'].mean(), test_df['label'].mean()<jupyter_output><empty_output><jupyter_text>Text Length （Char-level，non-preprocessed）<jupyter_code>text_1_lens = all_df['text_1'].apply(lambda x: len(x.strip()))
text_2_lens = all_df['text_2'].apply(lambda x: len(x.strip()))
text_lens = pd.concat([text_1_lens, text_2_lens], ignore_index=True)
text_lens.head()
text_lens.describe([0.05, 0.1, .80, 0.90, .95, .99])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.19486404833836857, 'fraction_numerical': 0.030211480362537766, 'mean_word_length': 11.75, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '497099', 'license': 'Apache-2.0', 'n_tokens_mistral': 289, 'n_tokens_neox': 272, 'n_words': 27, 'path': 'fine_tune/notebooks/LCQMC/EDA.ipynb', 'repo_name': 'IceFlameWorm/pytorch-pretrained-BERT', 'size': '662'}