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app.py

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+import gradio as gr
+from transformers import pipeline
+
+unmasker = pipeline('fill-mask', model='bert-base-uncased')
+
+def fill_mask(text):
+  unmasked = unmasker(text)
+  output = {}
+  for unmask in unmasked:
+    output[unmask["token_str"]] = unmask["score"]
+  return output
+
+examples = [["Hello I'm a [MASK] model."], ["[MASK] is my favourite sports."]]
+
+demo = gr.Interface(fn=fill_mask, inputs=gr.Textbox(lines=1, label="Input"), outputs=gr.Label(label="Output"), examples=examples)
+demo.launch()

requirements.txt

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+gradio
+torch
+timm
+sentencepiece
+transformers