<jupyter_start><jupyter_text>TEST<jupyter_code>df2 = pd.read_csv("../src/tests/test_set_120tweets_unlabeled.csv", nrows=240)
print(df2)<jupyter_output>Unnamed: 0 candidate      time             tweet_id  \
0             0  Pecresse  15:58:19  1505936851709747210   
1             1  Pecresse  15:57:11  1505936568510517252   
2             2  Pecresse  15:55:43  1505936200019980292   
3             3  Pecresse  15:55:40  1505936186719752194   
4             4  Pecresse  15:55:26  1505936127580155909   
..          ...       ...       ...                  ...   
115         115    Poutou  23:37:00  1505689895439851520   
116         116    Poutou  23:36:14  1505689703537852422   
117         117    Poutou  23:34:29  1505689262766927878   
118         118    Poutou  23:34:28  1505689260103503878   
119         119    Poutou  23:33:24  1505688991110160385   

                                                  text  Positive  Negative  
0    @lesRepublicains @vpecresse @avecValerie Votre...         0         0  
1    @avecValerie @vpecresse @ChJacob77 Les bal tri...         0         0  
2    @17eavecValerie @vpecresse @AureliAssouline[...]
{'fraction_non_alphanumeric': 0.06950477845351868, 'fraction_numerical': 0.2693310165073849, 'mean_word_length': 1.5714285714285714, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 3, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '600401', 'license': 'MIT', 'n_tokens_mistral': 593, 'n_tokens_neox': 419, 'n_words': 81, 'path': 'notebooks/Make_dataset_twitter_test.ipynb', 'repo_name': '42-AI/DemocracyWatcher', 'size': '1151'}