T5.py

# coding: UTF-8
import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer


class Config(object):
    """配置参数"""

    def __init__(self, dataset, pretrained_name_or_path=None):
        self.model_name = 'T5'
        self.train_path = dataset + '/data/train.txt'  # 训练集
        self.dev_path = dataset + '/data/dev.txt'  # 验证集
        self.test_path = dataset + '/data/test.txt'  # 测试集
        self.class_list = [x.strip() for x in open(
            dataset + '/data/class.txt').readlines()]  # 类别名单
        self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'  # 模型训练结果
        self.log_path = dataset + '/log/' + self.model_name
        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.multi_label = False
        self.num_classes = len(self.class_list)  # 类别数
        self.num_epochs = 3  # epoch数
        self.batch_size = 128  # 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.encoder_path = './t5_pretrain' if not pretrained_name_or_path else pretrained_name_or_path
        self.tokenizer = AutoTokenizer.from_pretrained(self.encoder_path)
        self.hidden_size = 768


class Model(nn.Module):

    def __init__(self, config):
        super(Model, self).__init__()
        self.encoder = AutoModel.from_pretrained(config.encoder_path)
        self.tokenizer = config.tokenizer
        self.device = config.device
        for param in self.encoder.parameters():
            param.requires_grad = True
        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]
        # ref:https://discuss.huggingface.co/t/t5-classification-using-text2text/504/8
        decoder_input_ids = torch.tensor([self.tokenizer.pad_token_id]).unsqueeze(0).expand(context.size(0), -1).to(
            self.device)
        # outputs:torch.Size([128, 1, 768])
        outputs = self.encoder(context, attention_mask=mask, decoder_input_ids=decoder_input_ids,
                               return_dict=True).last_hidden_state
        # token_ids = torch.argmax(outputs, dim=2)
        # tokens = self.tokenizer.batch_decode(token_ids)

        outputs = self.fc(outputs.squeeze())
        return outputs