AlBERT模型微调_albert微调 知乎

import torch
import torch.nn as nn
import os
from transformers import AlbertModel, BertTokenizer, AlbertConfig
 
 
class Config(object):
    def __init__(self, dataset):
        self.model_name = "albert"
        self.data_path = "./albert/data/data/"
        self.train_path = self.data_path + "train.txt"  # 训练集
        self.dev_path = self.data_path + "dev.txt"  # 验证集
        self.test_path = self.data_path + "test.txt"  # 测试集
        self.class_list = [
            x.strip() for x in open(self.data_path + "class.txt").readlines()
        ]  # 类别名单
        self.save_path = "./albert/src/saved_dic"
        if not os.path.exists(self.save_path):
            os.mkdir(self.save_path)
        self.save_path += "/" + self.model_name + ".pt"  # 模型训练结果
        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)  # 类别数
        self.num_epochs = 5  # epoch数
        self.batch_size = 256  # 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 = "/home/ec2-user/toutiao/albert/data/albert_chinese_base/"
        self.tokenizer = BertTokenizer.from_pretrained(self.bert_path)
        self.bert_config = AlbertConfig.from_pretrained(self.bert_path + '/config.json')
        self.hidden_size = 768
 
 
class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        self.albert = AlbertModel.from_pretrained(config.bert_path,config=config.bert_config)
 
        for name, param in self.albert.named_parameters():
            param.requires_grad = True
            print(name)
 
        self.fc = nn.Linear(config.hidden_size, config.num_classes)
 
    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.albert(context, attention_mask=mask)
        out = self.fc(pooled)
        return out