Added TempScalePipe back in

app.py

@@ -4,26 +4,29 @@ from transformers import pipeline, Pipeline
 
 unmasker = pipeline("fill-mask", model="anferico/bert-for-patents")
 
-example = 'A crustless sandwich made from two slices of baked bread'
-
-# def add_mask(text, size=1):
-#     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)
-
-# class TempScalePipe(Pipeline):
-#     def _forward(self, model_inputs):
-#         outputs = self.model(**model_inputs)
-#         return outputs
-#
-#     def postprocess(self, model_outputs, temp=1e3):
-#         out = model_outputs["logits"] / temp
-#         idx = np.random.randint(-3, 0)
-#         best_class = out.softmax(idx)
-#         return best_class
-#
+example = 'A crustless [MASK] made from two slices of baked bread'
+
+
+def add_mask(text, size=1):
+    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)
+
+
+class TempScalePipe(Pipeline):
+    def _forward(self, model_inputs):
+        outputs = self.model(**model_inputs)
+        return outputs
+
+    def postprocess(self, model_outputs, temp=1e3):
+        out = model_outputs["logits"] / temp
+        best_class = out.softmax(-1)
+        print(out)
+        probas = np.random.multinomial(1, best_class, 1)
+        return np.argmax(probas)
+
 
 def unmask(text):
     # text = add_mask(text)