python bert code fix

pytorch-bert-code/bert.py

@@ -1,30 +1,29 @@
 # coding: UTF-8
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 # from pytorch_pretrained_bert import BertModel, BertTokenizer
-from transformers import BertModel, BertTokenizer
-
+from transformers import BertModel, BertTokenizer,BertConfig
+import os
 
 class Config(object):
 
     """配置参数"""
     def __init__(self, dataset):
         self.model_name = 'bert'
-        self.train_path = dataset + '/data/train.txt'
-        self.dev_path = dataset + '/data/dev.txt'
-        self.test_path = dataset + '/data/test.txt'
+        self.train_path = dataset + '/data/train.txt'                                # 训练集
+        self.dev_path = dataset + '/data/dev.txt'                                    # 验证集
+        self.test_path = dataset + '/data/test.txt'                                  # 测试集
         self.class_list = [x.strip() for x in open(
-            dataset + '/data/class.txt').readlines()]
-        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+            dataset + '/data/class.txt').readlines()]                                # 类别名单
+        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'        # 模型训练结果
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备
 
-
-        self.num_classes = len(self.class_list)
-        self.num_epochs = 3
-        self.batch_size = 128
-        self.pad_size = 32
-        self.learning_rate = 5e-5
+        self.require_improvement = 1000                                 # 若超过1000batch效果还没提升，则提前结束训练
+        self.num_classes = len(self.class_list)                         # 类别数
+        self.num_epochs = 3                                             # epoch数
+        self.batch_size = 128                                           # mini-batch大小
+        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
+        self.learning_rate = 5e-5                                       # 学习率
         self.bert_path = './bert'
         self.tokenizer = BertTokenizer.from_pretrained(self.bert_path)
         self.hidden_size = 768
@@ -34,20 +33,16 @@ class Model(nn.Module):
 
     def __init__(self, config):
         super(Model, self).__init__()
-        self.bert = BertModel.from_pretrained(config.bert_path)
+        bert_config_file = os.path.join(config.bert_path, f'bert_config.json')
+        bert_config = BertConfig.from_json_file(bert_config_file)
+        self.bert = BertModel.from_pretrained(config.bert_path,config=bert_config)
         for param in self.bert.parameters():
             param.requires_grad = True
         self.fc = nn.Linear(config.hidden_size, config.num_classes)
 
-
-    def forward(self, input_ids,# 输入的句子
-                input_mask,# 对padding部分进行mask，和句子一个size，padding部分用0表示，如：[1, 1, 1, 1, 0, 0]
-                segments_ids
-                ):
-        _, pooled = self.bert(input_ids, attention_mask=input_mask,token_type_ids=segments_ids)#pooled [batch_size, hidden_size]
+    def forward(self, x):
+        context = x[0]  # 输入的句子
+        mask = x[2]  # 对padding部分进行mask，和句子一个size，padding部分用0表示，如：[1, 1, 1, 1, 0, 0]
+        _, pooled = self.bert(context, attention_mask=mask)
         out = self.fc(pooled)
         return out
-    def loss(self,outputs,labels):
-        criterion=F.cross_entropy
-        loss = criterion(outputs, labels)
-        return loss