Add a PyQt GUI & improve some code

.gitignore

@@ -0,0 +1,6 @@
+__pycache__/
+
+*.log
+*.bin
+
+*.sqlite3

BERT_CRF.py → BERT_CRF_Model.py

@@ -10,11 +10,6 @@
 VOB_NAME = "bert-base-chinese-vocab.txt"
 
 
-
-
-
-
-
 class BertCrf(nn.Module):
     def __init__(self,config_name:str,model_name:str = None,num_tags: int = 2, batch_first:bool = True) -> None:
         # 记录batch_first
@@ -58,7 +53,6 @@ def __init__(self,config_name:str,model_name:str = None,num_tags: int = 2, batch
         self.crf_model = CRF(num_tags=num_tags,batch_first=batch_first)
 
 
-
     def forward(self,input_ids:torch.Tensor,
                 tags:torch.Tensor = None,
                 attention_mask:Optional[torch.ByteTensor] = None,
@@ -68,29 +62,23 @@ def forward(self,input_ids:torch.Tensor,
 
         emissions = self.bertModel(input_ids = input_ids,attention_mask = attention_mask,token_type_ids=token_type_ids)[0]
 
-        # 这里在seq_len的维度上去头，是去掉了[CLS]，去尾巴有两种情况
-        # 1、是 <pad> 2、[SEP]
-
-
+        # 这里在seq_len的维度上去掉开头，是去掉了[CLS]，去尾巴有两种情况
+        # 第一种情况是 <pad>、第二种情况是 [SEP]
         new_emissions = emissions[:,1:-1]
         new_mask = attention_mask[:,2:].bool()
 
-        # 如果 tags 为 None，表示是一个预测的过程，不能求得loss,loss 直接为None
+        # 如果 tags 为 None，表示是一个预测的过程，不能求得 loss，则 loss 的值直接为 None
         if tags is None:
             loss = None
             pass
         else:
             new_tags = tags[:, 1:-1]
             loss = self.crf_model(emissions=new_emissions, tags=new_tags, mask=new_mask, reduction=reduction)
 
-
-
         if decode:
             tag_list = self.crf_model.decode(emissions = new_emissions,mask = new_mask)
             return [loss, tag_list]
 
         return [loss]
 
 
-
-

CRF_Model.py

@@ -2,6 +2,9 @@
 import torch
 import torch.nn as nn
 
+"""
+CRF 条件随机场模型；
+"""
 
 class CRF(nn.Module):
     def __init__(self,num_tags : int = 2, batch_first:bool = True) -> None:
@@ -10,7 +13,8 @@ def __init__(self,num_tags : int = 2, batch_first:bool = True) -> None:
         super().__init__()
         self.num_tags = num_tags
         self.batch_first = batch_first
-        # start 到其他tag(不包含end)的得分
+        # start 到其他 tag (不包含 end) 的得分
+        # (从开始节点到其他非 end 节点的 scores)
         self.start_transitions = nn.Parameter(torch.empty(num_tags))
         # 到其他tag(不包含start)到end的得分
         self.end_transitions = nn.Parameter(torch.empty(num_tags))
@@ -21,6 +25,7 @@ def __init__(self,num_tags : int = 2, batch_first:bool = True) -> None:
 
         self.reset_parameters()
 
+    # 对参数进行重新设置
     def reset_parameters(self):
         init_range = 0.1
         nn.init.uniform_(self.start_transitions,-init_range,init_range)
@@ -30,6 +35,7 @@ def reset_parameters(self):
     def __repr__(self):
         return f'{self.__class__.__name__}(num_tags={self.num_tags})'
 
+    # 向前传播；
     def forward(self, emissions:torch.Tensor,
                 tags:torch.Tensor = None,
                 mask:Optional[torch.ByteTensor] = None,
@@ -42,6 +48,7 @@ def forward(self, emissions:torch.Tensor,
             raise ValueError(f'invalid reduction {reduction}')
 
         if mask is None:
+            #生成值全为1的张量，用于掩码
             mask = torch.ones_like(tags,dtype = torch.uint8)
     # a.shape (seq_len,batch_size)
     # a[0] shape ? batch_size
@@ -81,10 +88,6 @@ def decode(self,emissions:torch.Tensor,
         return self._viterbi_decode(emissions,mask)
 
 
-
-
-
-
     def _validate(self,
                   emissions:torch.Tensor,
                   tags:Optional[torch.LongTensor] = None ,
@@ -146,7 +149,7 @@ def _computer_score(self,
         # 这里是为了获取每一个样本最后一个词的tag。
         # shape: (batch_size,)   每一个batch 的真实长度
         seq_ends = mask.long().sum(dim=0) - 1
-        # 每个样本最火一个词的tag
+        # 每个样本最后一个词的tag
         last_tags = tags[seq_ends,torch.arange(batch_size)]
         # shape: (batch_size,) 每一个样本到最后一个词的得分加上之前的score
         score += self.end_transitions[last_tags]
@@ -250,4 +253,4 @@ def _viterbi_decode(self,emissions : torch.FloatTensor ,
 
             best_tags.reverse()
             best_tags_list.append(best_tags)
-        return best_tags_list
+        return best_tags_list

test_NER.py → NERTest.py

@@ -1,15 +1,15 @@
-from BERT_CRF import BertCrf
+from BERT_CRF_Model import BertCrf
 from transformers import BertTokenizer
-from NER_main import NerProcessor,statistical_real_sentences,flatten,CrfInputFeatures
+from NERTrain import NerProcessor,statistical_real_sentences,flatten,CrfInputFeatures
 from torch.utils.data import DataLoader, RandomSampler,TensorDataset
 from sklearn.metrics import classification_report
 import torch
 import numpy as np
 from tqdm import tqdm, trange
 
-
-
-
+"""
+对命名实体识别模型进行简单的测试
+"""
 
 processor = NerProcessor()
 tokenizer_inputs = ()
@@ -81,4 +81,4 @@
 #
 #    micro avg   0.996142  0.996142  0.996142    145137
 #    macro avg   0.994650  0.994380  0.994512    145137
-# weighted avg   0.996149  0.996142  0.996143    145137
+# weighted avg   0.996149  0.996142  0.996143    145137