<jupyter_start><jupyter_text>WITH RESPECT TO "backdoor_acc" PARAMETER KRUM DEFENSE VS MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('backdoor_acc(k)','backdoor_acc(mk)')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "main_task_acc" PARAMETER KRUM DEFENSE VS MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('main_task_acc(k)','main_task_acc(mk)')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "raw_task_acc" PARAMETER KRUM DEFENSE VS MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('raw_task_acc(k)','raw_task_acc(mk)')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "wg_norm" PARAMETER KRUM DEFENSE VS MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('wg_norm(k)','wg_norm(mk)')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "adv_norm_diff" PARAMETER KRUM DEFENSE VS MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('adv_norm_diff(k)','adv_norm_diff(mk)')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "main_task_acc" PARAMETER KRUM DEFENSE<jupyter_code>data.plot.scatter('main_task_acc(k)','fl_iter')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "main_task_acc" PARAMETER  MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('main_task_acc(mk)','fl_iter')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "backdoor_acc" PARAMETER KRUM DEFENSE<jupyter_code>data.plot.scatter('backdoor_acc(k)','fl_iter')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "backdoor_acc" PARAMETER MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('backdoor_acc(mk)','fl_iter')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "raw_task_acc" PARAMETER KRUM DEFENSE<jupyter_code>data.plot.scatter('raw_task_acc(k)','fl_iter')<jupyter_output><empty_output><jupyter_text>WITH RESPECT TO "raw_task_acc" PARAMETER MULTIKRUM DEFENSE<jupyter_code>data.plot.scatter('raw_task_acc(mk)','fl_iter')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12749213011542498, 'fraction_numerical': 0.0, 'mean_word_length': 17.16190476190476, 'pattern_counts': {'":': 0, '<': 45, '<?xml version=': 0, '>': 45, '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': '11', 'dir': 'jupyter-structured-clean-dedup', 'id': '139588', 'license': 'MIT', 'n_tokens_mistral': 799, 'n_tokens_neox': 795, 'n_words': 82, 'path': 'results/multikrum vs krum.ipynb', 'repo_name': 'jaintj95/Federated_AoT', 'size': '1906'}