<jupyter_start><jupyter_text>Sanity<jupyter_code>df_train_final = pd.DataFrame(train_mentions)
df_train_final.head()
count_df = pd.DataFrame(df_train_final.groupby('mention')['mention_id'].count()).reset_index(level=['mention'])
count_df[count_df['mention_id'] > 1]
count = 0
total_length = 0
for i in range(df_train_final.shape[0]):
    total_length += len(df_train_final['mention'].iloc[i])
    if len(df_train_final['mention'].iloc[i]) < 34:
        count += 1
        print(df_train_final['mention'].iloc[i])

print(count)      
df_val_final = pd.DataFrame(val_mentions)
df_val_final[df_val_final['mention'].isin(df_train_final['mention'])]
df_test_final = pd.DataFrame(test_mentions)
df_test_final[df_test_final['mention'].isin(df_train_final['mention'])]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15822784810126583, 'fraction_numerical': 0.008860759493670886, 'mean_word_length': 8.305882352941177, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, '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': '46545', 'license': 'MIT', 'n_tokens_mistral': 319, 'n_tokens_neox': 308, 'n_words': 30, 'path': 'blink/preprocess/Create_mention_jsons_Original_Tweets.ipynb', 'repo_name': 'parin1995/claim2fact', 'size': '790'}