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| import urllib.request | |
| import gradio as gr | |
| import numpy as np | |
| from utils import similarity | |
| import scipy | |
| def load_glove(path): | |
| with open(path) as f: | |
| lines = f.readlines() | |
| wv = [] | |
| vocab = [] | |
| for line in lines: | |
| tokens = line.strip().split(" ") | |
| assert len(tokens) == 301 | |
| vocab.append(tokens[0]) | |
| wv.append([float(elem) for elem in tokens[1:]]) | |
| w2i = {w: i for i, w in enumerate(vocab)} | |
| wv = np.array(wv).astype(float) | |
| print(len(vocab), wv.shape, len(w2i)) | |
| return wv, w2i, vocab | |
| urllib.request.urlretrieve("https://cdn-lfs.huggingface.co/repos/51/d0/51d02f0735de2187e78af7db593c8d71efbcddfe9f5e45cfa1c77904daf0dfdf/20dac974c73413d4b4f463f9b28eabfb326793d2732c108a9cd1262f2a055dc3?response-content-disposition=attachment%3B%20filename%3D%22glove_debiased_300d.txt%22", "glove_debiased_300d.txt") | |
| urllib.request.urlretrieve("https://cdn-lfs.huggingface.co/repos/51/d0/51d02f0735de2187e78af7db593c8d71efbcddfe9f5e45cfa1c77904daf0dfdf/91125602f730fea7ca768736c6f442e668b49db095682bf2aad375db061c21ed?response-content-disposition=attachment%3B%20filename%3D%22glove.6B.300d.txt%222", "glove_300d.txt") | |
| original_wv, original_w2i, original_vocab = load_glove('glove_300d.txt') | |
| wv_debiased, w2i_debiased, vocab_debiased = load_glove('glove_debiased_300d.txt') | |
| def debias(gendered_word1,gendered_word2,occupation,model): | |
| # if model == 'Glove-300d' : | |
| # original_wv, original_w2i, original_vocab = load_glove('glove_300d.txt') | |
| # wv_debiased, w2i_debiased, vocab_debiased = load_glove('glove_debiased_300d.txt') | |
| print(similarity('man', 'nurse', original_wv, original_w2i)) | |
| return abs(similarity(gendered_word1, occupation, wv_debiased, w2i_debiased)-similarity(gendered_word2, occupation, wv_debiased, w2i_debiased)), \ | |
| abs(similarity(gendered_word1, occupation, original_wv, original_w2i)-similarity(gendered_word2, occupation, original_wv, original_w2i)), "<h3>How the difference is measured?</h3> <p>abs(similarity(gendered_word1,occupation)-similarity(gendered_word2,occupation))</p>" | |
| demo = gr.Interface( | |
| debias, | |
| inputs = [ | |
| gr.Textbox(placeholder="Gendered Word 1(Exp. man)"), | |
| gr.Textbox(placeholder="Gendered Word 2(Exp. woman)"), | |
| gr.Textbox(placeholder="Occupation(Exp. nurse)"), | |
| gr.Radio(choices=['Glove-300d'],value='Glove-300d') | |
| ], | |
| outputs = [gr.Textbox(label="Absolute similarity differece in the debiased version"),gr.Textbox(label="Absolute similarity differece in the original version"), gr.Markdown( | |
| value="<h3>How the difference is measured?</h3> <p>abs(similarity(gendered_word1,occupation)-similarity(gendered_word2,occupation))</p>")], | |
| description = '<a href="https://aclanthology.org/2020.acl-main.484/">Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation</a>' | |
| ) | |
| if __name__ == '__main__': | |
| demo.launch() | |