还原删除代码

src/py3.x/tensorflow2.x/text_bert.py

@@ -93,6 +93,64 @@ if __name__ == "__main__":
 ## bert / Embedding/  + lstm + crt
 
 
+ #%%
+ # 加载数据
+ class TextBert():
+     def __init__(self):
+         self.path_config = Config.bert.path_config
+         self.path_checkpoint = Config.bert.path_checkpoint
+
+         self.token_dict = {}
+         with codecs.open(Config.bert.dict_path, 'r', 'utf8') as reader:
+             for line in reader:
+                 token = line.strip()
+                 self.token_dict[token] = len(self.token_dict)
+
+
+     def prepare_data(self):
+         neg = pd.read_excel(Config.bert.path_neg, header=None)
+         pos = pd.read_excel(Config.bert.path_pos, header=None)
+         data = []
+         for d in neg[0]:
+             data.append((d, 0))
+         for d in pos[0]:
+             data.append((d, 1))
+         # 按照9:1的比例划分训练集和验证集
+         random_order = list(range(len(data)))
+         np.random.shuffle(random_order)
+         train_data = [data[j] for i, j in enumerate(random_order) if i % 10 != 0]
+         valid_data = [data[j] for i, j in enumerate(random_order) if i % 10 == 0]
+         return train_data, valid_data
+
+     def build_model(self, m_type="bert"):
+         if m_type == "bert":
+             bert_model = load_trained_model_from_checkpoint(self.path_config, self.path_checkpoint, seq_len=None)
+             for l in bert_model.layers:
+                 l.trainable = True
+             x1_in = Input(shape=(None,))
+             x2_in = Input(shape=(None,))
+             x = bert_model([x1_in, x2_in])
+             x = Lambda(lambda x: x[:, 0])(x)
+             p = Dense(1, activation='sigmoid')(x)#根据分类种类自行调节，也可以多加一些层数
+             model = Model([x1_in, x2_in], p)
+             model.compile(
+                 loss='binary_crossentropy',
+                 optimizer=Adam(1e-5), # 用足够小的学习率
+                 metrics=['accuracy']
+             )
+         else:
+             # 否则用 Embedding
+             model = Sequential()
+             model.add(Embedding(len(vocab), EMBED_DIM, mask_zero=True))  # Random embedding
+             model.add(Bidirectional(LSTM(BiRNN_UNITS // 2, return_sequences=True)))
+             crf = CRF(len(chunk_tags), sparse_target=True)
+             model.add(crf)
+             model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
+
+         model.summary()
+         return model
+
+
 #%%
 # 加载数据
 from keras_bert import Tokenizer