<jupyter_start><jupyter_text>Religion (MISC)<jupyter_code>run_and_save_res("grouped_religion", model_name, task="NER")
load_and_plot("grouped_religion", model_name, cl="MISC")<jupyter_output><empty_output><jupyter_text>Nationality (LOC)<jupyter_code>run_and_save_res("grouped_country_by_gdp_ppp_quantile", model_name, task="NER")
load_and_plot("grouped_country_by_gdp_ppp_quantile", model_name, cl="LOC")<jupyter_output>WARNING:checklist_fork.checklist.graphs.graphs:Missing results for test fped.
ERROR:checklist_fork.checklist.graphs.graphs:Reason: FPR not available for the task, seq:True
NoneType: None
WARNING:checklist_fork.checklist.graphs.graphs:Missing results for test fpr_ratio_bcm.
ERROR:checklist_fork.checklist.graphs.graphs:Reason: FPR not available for the task, seq:True
NoneType: None
WARNING:checklist_fork.checklist.graphs.graphs:Missing results for test tnr_gap_pcm.
ERROR:checklist_fork.checklist.graphs.graphs:Reason: TNR not available for the task, seq:True
NoneType: None
WARNING:checklist_fork.checklist.graphs.graphs:Missing results for test aeg.
ERROR:checklist_fork.checklist.graphs.graphs:Reason: use_pos_score_in_binary_sent is True but classification is not binary.
NoneType: None<jupyter_text>By Region<jupyter_code>run_and_save_res("grouped_country_by_region", model_name, task="NER")
load_and_plot("grouped_country_by_region", model_name, cl="LOC")<jupyter_output><empty_output><jupyter_text>Gender (Names)  (PER)<jupyter_code>run_and_save_res("names_gender", model_name, task="NER")
load_and_plot("names_gender", model_name, cl="PER")
suite = test_model_on_saved_suite(
    suite_name="names_gender",
    mname=model_name,
    #repred=True,
    tests=[BasicSeqMetrics()],
    task="NER"
)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10946408209806158, 'fraction_numerical': 0.0, 'mean_word_length': 11.10344827586207, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '110037', 'license': 'Apache-2.0', 'n_tokens_mistral': 654, 'n_tokens_neox': 618, 'n_words': 94, 'path': 'notebooks/SeqLabeling.ipynb', 'repo_name': 'pczarnowska/generalized-fairness-metrics', 'size': '1754'}