<jupyter_start><jupyter_text>Give Paths or Required Names<jupyter_code>print(Acc_Sheet.__doc__)
data_path = "BERT_production_tags_JD_mergedkeys.csv" #ENTER DATA PATH
tech_path = "Tech_pydictionary_final_JD.csv"
eng_path = "Eng_pydictionary_final_JD.csv"
ID = 'job_id'                                # ID column name
text_col = 'job_desc'                       # column name on which predictions are made
manually_tagged = 'prod_tag'                 #manual tag column name
bert_tags = 'BERT_Tags'                      #column name

obj_name = Acc_Sheet(data_path,tech_path,eng_path,ID,text_col,manually_tagged,bert_tags)

temp = obj_name.ENG_SHEET()    ##########GIVE ENG ACC NAME############
print("ENG_SHEET_NAME", temp['TP'].sum(), temp['FP'].sum(), temp['FN'].sum())
ENG_SHEET_NAME = "ACC-BERT_TITLE_ENG_ONLY_DATE.csv"
# temp.to_csv(ENG_SHEET_NAME,index=False)
temp = obj_name.TECH_SHEET()
TECH_SHEET_NAME = "ACC-TECH_SM_TITLE_DATE.csv"  ##########GIVE TECH ACC NAME############

print(TECH_SHEET_NAME, temp['TP'].sum(), temp['FP'].sum(), temp['FN'].sum())
# temp.to_csv(TECH_SHEET_NAME,index=False)
temp = obj_name.ENG_TECH_SHEET()
ENG_TECH_SHEET_NAME = "ACC-ENG_TECH_SM_TITLE_DATE.csv"  ##########GIVE ENG+TECH ACC NAME############

print(ENG_TECH_SHEET_NAME, temp['TP'].sum(), temp['FP'].sum(), temp['FN'].sum())
# temp.to_csv(ENG_TECH_SHEET_NAME,index=False)<jupyter_output><ipython-input-2-130133c4798a>:67: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[column_name][i] = res
<ipython-input-2-130133c4798a>:418: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_cm[col_name_op][i] = [0 if element == '0' else element for element in l]
{'fraction_non_alphanumeric': 0.15304606240713226, 'fraction_numerical': 0.014363546310054482, 'mean_word_length': 5.102719033232629, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '545250', 'license': 'MIT', 'n_tokens_mistral': 805, 'n_tokens_neox': 745, 'n_words': 141, 'path': 'BERT/CLASS_All_Accuracy_sheet_GENERATOR.ipynb', 'repo_name': 'mohit-bags/BERT-Notebooks', 'size': '2019'}