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import load_model_pt
import interpret_model_pt
def sub_pipeline(raw_input, pretrained_model):
tokenizer, model = load_model_pt.load_models_from_pretrained(pretrained_model)
output_ = load_model_pt.load_pipeline(raw_input, pretrained_model)
words_weightages = interpret_model_pt.explainer(raw_input, model, tokenizer)
return output_, words_weightages
def bias_checker(input_statement):
pretrained_model_basic_check = "valurank/distilroberta-bias"
pretrained_model_political = "valurank/distilroberta-mbfc-bias"
pretrained_model_gender = "monologg/koelectra-base-v3-gender-bias"
raw_input = input_statement
# print("Checking if the input has any primary bias ?..")
output_stmt_zero, words_interpreted = sub_pipeline(raw_input, pretrained_model_basic_check)
print(output_stmt_zero)
return_var = " "
interpret_var = " "
if (output_stmt_zero["label"] == "BIASED" and output_stmt_zero["score"] >= 0.7) or (output_stmt_zero["label"] == "NEUTRAL" and output_stmt_zero["score"] < 0.6):
# print(output_stmt_zero)
# print("\n The statement seems biased, lets investigate ! \n")
# print(words_interpreted)
# print("\n Checking for political propaganda... \n")
output_stmt_political, words_interpreted_political = sub_pipeline(raw_input, pretrained_model_political)
# print(output_stmt_political, "\n")
# print(words_interpreted_political, "\n")
# print("\n Let's check for gender bias, shall we ? \n")
output_stmt_gender, words_interpreted_gender = sub_pipeline(raw_input, pretrained_model_gender)
# print(output_stmt_gender, "\n")
# print(words_interpreted_gender, "\n")
return_var = ("Generic:", output_stmt_zero,"\n","Gender:", output_stmt_gender,"\n","Political:", output_stmt_political)
interpret_var = ("Generic:", words_interpreted, "\n", "Gender:", words_interpreted_gender, "\n","Political:", words_interpreted_political)
else:
# print("The statement seems ok as of now, please input another statement!")
return_var = "The statement seems ok as of now, please input another statement!"
interpret_var = " "
return return_var, interpret_var
if __name__=="__main__":
input_stmt = "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property."
bias_checker(input_stmt)
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