valinself.encodings.items()}item['labels']=torch.tensor(self.labels[idx])returnitemdef__len__(self):returnlen(self.labels)# 假设我们有这些数据texts=["I love programming!","I hate bugs."]labels=[1,0]encodings=tokenizer(texts,truncation=True,padding=True,return_tensors="pt")dataset=TextDat...
valinself.encodings.items()}item['labels']=torch.tensor(self.labels[idx])returnitemdef__len__(self):returnlen(self.labels)# 创建数据集和DataLoadertrain_dataset=SentimentDataset(train_encodings,y_train)train_loader=DataLoader(train_dataset,batch_size=16,shuffle=True)# 定义优化器和损失函数optimizer=...
Python里面的解码和编码也就是unicode和str这两种形式的相互转化。解码就是str -> unicode，相反的，编码...
# and pad with 0's when less than `max_length` train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=max_length) valid_encodings = tokenizer(valid_texts, truncation=True, padding=True, max_length=max_length) 我们设置truncation以True使我们消除上面去令牌max_length,...
train_dataset = SquadDataset(train_encodings) train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True) # 优化器 optim = torch.optim.AdamW(model.parameters(), lr=args.lr) # 可视化部署 swanlab.init( project="Bert_fine-tuning", ...
python BertTokenizer -编码和解码序列时出现额外空格如果您尝试使用BERT进行标记分类，以便在原始字符串中...
(train_data, truncation=True, padding=True) train_dataset = CommentDataset(train_encodings, train_labels) train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True) # 加载开发集数据 dev_data = [] dev_labels = [] with open('Dataset/dev.txt', 'r', encoding='utf-8') as f:...
compile(model, inputs=inputs, enabled_precisions=enabled_precisions, truncate_long_and_double=True ) input_data = train_encodings.to("cuda") with torch.no_grad(): result = trt_model(input_data['input_ids'], input_data['token_type_ids'], input_data['attention_mask']) torch.jit.save...
python BertTokenizer -编码和解码序列时出现额外空格如果您尝试使用BERT进行标记分类，以便在原始字符串中...
代码运行次数:0 运行 AI代码解释 classAttention(nn.Module):""" Scaled Dot Product Attention""" defforward(self,query,key,value,mask=None,dropout=None):scores=torch.matmul(query,key.transpose(-2,-1))\/math.sqrt(query.size(-1))ifmask is not None:scores=scores.masked_fill(mask==0,-1e9)...