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| import torch | |
| from modeling import GPT2Wrapper | |
| import gradio as gr | |
| DEBIASING_PREFIXES = [ | |
| "The following text contains rude, disrespectful, or unreasonable language:\n", | |
| "The following text contains very hateful, aggressive, disrespectful language:\n", | |
| "The following text contains sexually explicit language:\n", | |
| "The following text contains a threat:\n", | |
| "The following text contains negative or hateful language targeting someone because of their identity:\n", | |
| "The following text contains swear words, curse words, or other obscene or profane language:\n" | |
| ] | |
| DEBIASING_KEYWORDS = [ | |
| "(rude) ", "(sexually explicit) ", "(hateful) ", "(aggressive) ", "(racist) ", "(threat) ", "(violent) ", "(sexist) " | |
| ] | |
| def debias(prompt, model,use_prefix, max_length=50, num_beam=3): | |
| """ | |
| Debiasing inference function. | |
| :param prompt: The prompt to be debiased. | |
| :param model: The GPT2 model. | |
| :param max_length: The maximum length of the output sentence. | |
| :return: The debiased output sentence. | |
| """ | |
| wrapper = GPT2Wrapper(model_name=str(model), use_cuda=False) | |
| if use_prefix == 'Prefixes': | |
| debiasing_prefixes = DEBIASING_PREFIXES | |
| else: | |
| debiasing_prefixes = DEBIASING_KEYWORDS | |
| output_text = output_text = wrapper.generate_self_debiasing([prompt], debiasing_prefixes= debiasing_prefixes,min_length=20, max_length=max_length, num_beam=num_beam,no_repeat_ngram_size=2) | |
| output_text = output_text[0] | |
| debiasing_prefixes = [] | |
| biased_text = wrapper.generate_self_debiasing([prompt], debiasing_prefixes= debiasing_prefixes,min_length=20, max_length=max_length, num_beam=num_beam,no_repeat_ngram_size=2) | |
| biased_text = biased_text[0] | |
| return output_text, biased_text | |
| demo = gr.Interface( | |
| debias, | |
| inputs = [gr.Textbox(), | |
| gr.Radio(choices=['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'],value='gpt2'), | |
| gr.Radio(choices=['Prefixes','Keywords'],value='Prefixes',label='Use Debiasing Prefixes or Keywords'), | |
| gr.Number(value=50,label='Max output length'), | |
| gr.Number(value=3,label='Number of beams for beam search')], | |
| outputs = [gr.Textbox(label="Debiased text"),gr.Textbox(label="Biased text")] | |
| ) | |
| if __name__ == '__main__': | |
| demo.launch() |