initial commit

app.py

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+import gradio as gr
+import numpy as np
+from transformers import pipeline
+
+unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")
+
+example = 'A crustless sandwich made from two slices of baked bread'
+
+def unmask(text):
+    res = unmasker(text)
+    out = {item["token_str"]: item["score"] for item in res}
+    return out
+
+
+textbox = gr.Textbox(label="Type language here", lines=5)
+import gradio as gr
+from transformers import pipeline
+
+unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")
+
+
+def add_mask(text, size=3):
+    split_text = text.split()
+    idx = np.random.randint(len(split_text), size=size)
+    for i in idx:
+        split_text[i] = '[MASK]'
+    return ' '.join(split_text)
+
+
+def unmask(text):
+    text = add_mask(text)
+    res = unmasker(text)
+    out = {item["token_str"]: item["score"] for item in res}
+    return out
+
+
+textbox = gr.Textbox(label="Type language here", lines=5)
+
+demo = gr.Interface(
+    fn=unmask,
+    inputs=textbox,
+    outputs="label",
+    examples=[
+
+    ],
+)
+
+demo.launch()
+demo = gr.Interface(
+    fn=unmask,
+    inputs=textbox,
+    outputs="label",
+    examples=[
+    ],
+)
+
+demo.launch()

requirements.txt

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+gradio
+torch
+transformers
+numpy