<jupyter_start><jupyter_text>Beer  UAC<jupyter_code># uac random
df = load_dataframe_csv("../tables/reproduce/beer/uac_random.csv","")
latex_table_row(df, table_name = "success_rate", value_precision = 4, uncertainty_precision = 3)
latex_table_row(df, table_name = "avg_length", value_precision = 3, uncertainty_precision = 3)
# uac diff
df = load_dataframe_csv("../tables/reproduce/beer/uac_diff.csv","")
latex_table_row(df, table_name = "success_rate", value_precision = 4, uncertainty_precision = 3)
latex_table_row(df, table_name = "avg_length", value_precision = 3, uncertainty_precision = 3)<jupyter_output>\begin{tabular}{llllll}
\toprule
{} &                1  &                5  &                10 &                20 &                50 \\
\midrule
0 &  0.0286$\pm$0.032 &  0.0528$\pm$0.035 &  0.0792$\pm$0.039 &  0.1093$\pm$0.042 &  0.2142$\pm$0.054 \\
\bottomrule
\end{tabular}

\begin{tabular}{llllll}
\toprule
{} &               1  &               5  &               10 &               20 &               50 \\
\midrule
0 &  9.806$\pm$0.216 &  9.622$\pm$0.263 &  9.349$\pm$0.331 &  9.075$\pm$0.359 &  8.082$\pm$0.484 \\
\bottomrule
\end{tabular}
{'fraction_non_alphanumeric': 0.15848406546080965, 'fraction_numerical': 0.09560723514211886, 'mean_word_length': 2.4894894894894897, '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': 7, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '535815', 'license': 'MIT', 'n_tokens_mistral': 509, 'n_tokens_neox': 442, 'n_words': 69, 'path': '.ipynb_checkpoints/Result in Latex form-checkpoint.ipynb', 'repo_name': 'litosly/llcrankresultanalysis', 'size': '1161'}