<jupyter_start><jupyter_text>ignore quoting<jupyter_code>df = pd.read_csv(UNL, engine='python', sep='\t', quoting=3, error_bad_lines=False, usecols=['text', 'timestamp'])
df
df_o = pd.concat([df, df_an1, df_an2]).drop_duplicates().dropna().sort_values(['timestamp'])
df_o['whatever'] = 1
df_o = df_o[['whatever', 'text']]
df_o
df_o[:200000].to_csv(str(OUT) + '/train.csv', index=False, header=False)
df_o[200000:].to_csv(str(OUT) + '/val.csv', index=False, header=False)
df_o
df_o['text']<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21235521235521235, 'fraction_numerical': 0.03088803088803089, 'mean_word_length': 9.8125, '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': '264505', 'license': 'MIT', 'n_tokens_mistral': 231, 'n_tokens_neox': 218, 'n_words': 30, 'path': 'code/ynacc/00 data/split unlabeled comments.ipynb', 'repo_name': 'jfilter/masters-thesis', 'size': '518'}