python bert code fix

pytorch-bert-code/bert-example.py

@@ -32,7 +32,7 @@ bert_config = BertConfig.from_json_file(bert_config_file)
 tokenizer = tokenization(vocab_file=vocab_file, do_lower_case=do_lower_case)
 
 # 加载模型
-model_bert =  BertModel.from_pretrained(bert_path)
+model_bert =  BertModel.from_pretrained(bert_path,config=bert_config)
 model_bert.to(device)
 
 

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.train_path = dataset + '/data/train.txt'                                # 训练集
-        self.dev_path = dataset + '/data/dev.txt'
+        self.dev_path = dataset + '/data/dev.txt'                                    # 验证集
-        self.test_path = dataset + '/data/test.txt'
+        self.test_path = dataset + '/data/test.txt'                                  # 测试集
         self.class_list = [x.strip() for x in open(
-            dataset + '/data/class.txt').readlines()]
+            dataset + '/data/class.txt').readlines()]                                # 类别名单
-        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'
+        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')
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备
 
-
+        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)
+        self.num_classes = len(self.class_list)                         # 类别数
-        self.num_epochs = 3
+        self.num_epochs = 3                                             # epoch数
-        self.batch_size = 128
+        self.batch_size = 128                                           # mini-batch大小
-        self.pad_size = 32
+        self.pad_size = 32                                              # 每句话处理成的长度(短填长切)
-        self.learning_rate = 5e-5
+        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, x):
-    def forward(self, input_ids,# 输入的句子
+        context = x[0]  # 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]
+        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]
-                segments_ids
+        _, pooled = self.bert(context, attention_mask=mask)
-                ):
-        _, pooled = self.bert(input_ids, attention_mask=input_mask,token_type_ids=segments_ids)#pooled [batch_size, hidden_size]
         out = self.fc(pooled)
         return out
-    def loss(self,outputs,labels):
-        criterion=F.cross_entropy
-        loss = criterion(outputs, labels)
-        return loss

pytorch-bert-code/bert/README.md

@@ -1,6 +1,18 @@
-### how to convert bert Converting Tensorflow Checkpoints to pytorch model file
+
+update to transformer 2.3.0
+
+转换工具已经失效
+
+ chinese bert   
+ 
+ https://github.com/ymcui/Chinese-BERT-wwm/blob/master/README_EN.md
+
+下载 BERT-wwm-ext, Chinese 或者 BERT-wwm, Chinese pytorch模型
 
 
+-------
+
+transformer 2.1.1
 ### 如何将bert model 的Tensorflow模型 转换为pytorch模型
 
 

pytorch-bert-code/run.py

@@ -2,7 +2,8 @@
 import time
 import torch
 import numpy as np
-from train_eval import train
+from train_eval import train, init_network
+from importlib import import_module
 import argparse
 from utils import build_dataset, build_iterator, get_time_dif
 import bert

pytorch-bert-code/train_eval.py

@@ -9,7 +9,23 @@ from utils import get_time_dif
 from transformers.optimization import AdamW
 
 
-
+# 权重初始化，默认xavier
+def init_network(model, method='xavier', exclude='embedding', seed=123):
+    for name, w in model.named_parameters():
+        if exclude not in name:
+            if len(w.size()) < 2:
+                continue
+            if 'weight' in name:
+                if method == 'xavier':
+                    nn.init.xavier_normal_(w)
+                elif method == 'kaiming':
+                    nn.init.kaiming_normal_(w)
+                else:
+                    nn.init.normal_(w)
+            elif 'bias' in name:
+                nn.init.constant_(w, 0)
+            else:
+                pass
 
 
 def train(config, model, train_iter, dev_iter, test_iter):