Downloading models

app.py

@@ -7,10 +7,26 @@ import gc
 bert_debiased = pipeline('fill-mask', model='Daniel-Saeedi/Sent-Debias-bert-gender-debiased')
 bert_original = pipeline('fill-mask', model='Daniel-Saeedi/Sent-Debias-bert-gender-debiased')
 
+def make_slider(unmask):
+    total_score = 0
+    for word in unmask:
+        total_score += word['score']
+
+    factor = 1/total_score
+
+    html = '<div>'
+
+    for word in unmask:
+        html += '<h3>{}</h3><input type="range" disable value={}><br>'.format(word['token_str'],factor*word['score'])
+    
+    html += '</div>'
+    return html
+    
+
 
 def fill_mask(stmt,model):
     if model == 'bert':
-        return bert_debiased(stmt), bert_original(stmt), ''
+        return make_slider(bert_debiased(stmt))
 
 
 demo = gr.Interface(
@@ -19,7 +35,7 @@ demo = gr.Interface(
         gr.Textbox(placeholder="Fill Mask"),
         gr.Radio(choices=['bert'],value='bert')
         ],
-        outputs = [gr.Textbox(label="Debiased:"),gr.Textbox(label="Original"), gr.Markdown(
+        outputs = [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>'
     )