更改为按照标签置信度排序

demo06_class_roformer_pred.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 import json
 import random
 import keras
@@ -74,12 +74,25 @@ def predict(text):
     sent_token_id, sent_segment_id = [], []
     token_ids, segment_ids = tokenizer.encode(text, maxlen=max_len)
     y_pred = model.predict([[token_ids], [segment_ids]])
-    idx = np.where(y_pred[0] > label_threshold, 1, 0)
+    # print(len(y_pred[0]))
+    label_probability = {}
+    for index, threshold in zip(range(len(y_pred[0])), label_threshold):
+        label_probability[index] = y_pred[0][index] - threshold
+    label_probability = sorted(label_probability.items(),
+                                key=lambda x: x[1], reverse=True)
     label_pre = []
-    for i in range(len(idx)):
+    for i in label_probability:
-        if idx[i] == 1:
+        if i[1] > 0:
-            label_pre.append(id2label[i])
+            label_pre.append(id2label[i[0]])
+    if label_pre == []:
+        label_pre.append(id2label[label_probability[0][0]])
     return label_pre
+    # idx = np.where(y_pred[0] > label_threshold, 1, 0)
+    # label_pre = []
+    # for i in range(len(idx)):
+    #     if idx[i] == 1:
+    #         label_pre.append(id2label[i])
+    # return label_pre
 
 
 if __name__ == '__main__':
@@ -101,5 +114,4 @@ if __name__ == '__main__':
         label_pre = "，".join(label_pre)
         data_new.append(data_dan + [label_pre])
     df = pd.DataFrame(data_new)
-    print(df)
     df.to_csv("./data/yy改写相似度含文章类别.csv", index=None)