随风而逝_似我飘零:

文章写得很好，代码有几处有几点优化建议，仅供参考[code=python]

class MyDataset(Dataset):

def __init__(self, texts, labels, max_length):

self.all_text = texts

self.all_label = labels

self.max_len = max_length

self.tokenizer = BertTokenizer.from_pretrained(parsers().bert_pred)

def __getitem__(self, index):

# 根据tokenizer生成文本中每个token的位置编码和掩码信息

result = self.tokenizer.encode_plus(text=self.all_text[index], max_length=self.max_len, padding='max_length',

truncation=True, return_tensors='pt')

# 标签信息

label = int(self.all_label[index])

# 将所有信息都转化为tensor处理

token_ids = result.input_ids

mask = result.attention_mask

label = torch.tensor(label)

return (token_ids, mask), label

def __len__(self):

# 得到文本的长度

return len(self.all_text)

[/code]

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[code=python]

def train():

# 加载参数

args = parsers()

device = "cuda:0" if torch.cuda.is_a

[/code]