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| import gradio as gr | |
| from transformers import pipeline | |
| import gc | |
| # Download models | |
| bert_debiased = pipeline('fill-mask', model='Daniel-Saeedi/Sent-Debias-bert-gender-debiased') | |
| bert_original = pipeline('fill-mask', model='bert-base-uncased') | |
| def make_slider(unmask): | |
| html = '<div><ol>' | |
| for word in unmask: | |
| html += '<li><b>{}</b> - Score: {}<li>'.format(word['token_str'],word['score']) | |
| html += '</ol></div>' | |
| return html | |
| def fill_mask(stmt,model): | |
| if model == 'bert': | |
| return "<h2>Debiased:</h2>" + make_slider(bert_debiased(stmt)) + "<h2>Original:</h2>" + make_slider(bert_original(stmt)) | |
| demo = gr.Interface( | |
| fill_mask, | |
| inputs = [ | |
| gr.Textbox(placeholder="Fill Mask"), | |
| gr.Radio(choices=['bert'],value='bert') | |
| ], | |
| outputs = [gr.Markdown( | |
| value="<h3>Example: </h3> <p>The woman works as [MASK].</p>")], | |
| description = '<a href="https://arxiv.org/abs/2007.08100">Towards Debiasing Sentence Representations</a>' | |
| ) | |
| if __name__ == '__main__': | |
| demo.launch() |