<jupyter_start><jupyter_text>Difference in classification<jupyter_code>class_diff = gru_inf - best_bench_class

class_diff = list(zip(names, class_diff))

for i in class_diff:
    print(i[0], "{:.1f}".format(i[1]))<jupyter_output>b1 0.0
b2 0.0
b3 20.5
b4 0.0
b5 35.4
b6 0.0
b11 14.3
b7 0.0
b8 15.8
b9 3.6
b10 1.5
b12 2.9
b13 1.9<jupyter_text>Difference in QA<jupyter_code>QA_diff = MemNN_inf - best_bench_QA

QA_diff = list(zip(names, QA_diff))

for i in QA_diff:
    print(i[0], "{:.1f}".format(i[1]))
dic_ave ={"names": names,
      "templates": templates,
      "vocab_size": vocab_size,
      "accuracy": average}


dic_classi ={"names": names,
      "templates": templates,
      "vocab_size": vocab_size,
      "accuracy": classi}


dic_QA ={"names": names,
      "templates": templates,
      "vocab_size": vocab_size,
      "accuracy": QA}


dic_best ={"names": names,
      "templates": templates,
      "vocab_size": vocab_size,
      "accuracy": best_average}

df_classi = pd.DataFrame(dic_classi)

df_QA = pd.DataFrame(dic_QA)

df_ave = pd.DataFrame(dic_ave)

df_best = pd.DataFrame(dic_best)



df_classi
df_classi.corr()
df_QA
df_QA.corr()
df_ave 
df_ave.corr()
df_best
df_best.corr()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1457654723127036, 'fraction_numerical': 0.04315960912052117, 'mean_word_length': 4.142259414225942, 'pattern_counts': {'":': 16, '<': 8, '<?xml version=': 0, '>': 8, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '447183', 'license': 'MIT', 'n_tokens_mistral': 588, 'n_tokens_neox': 546, 'n_words': 104, 'path': 'contraQA_final_results.ipynb', 'repo_name': 'felipessalvatore/ContraQA', 'size': '1228'}