Huawei Nezha实践指南

1,我们住的三人间，房间很宽敞，卫生间不大，但都很干净。宾馆餐厅很实惠，我们只要能回宾馆，就在餐厅用餐。周边交通非常方便，基本上一部车就到各个景点，走到什刹海等处也很近。最近宾馆在外部装修，不过不影响内部卫生和晚间休息。总体来说，我们住的都挺满意。
1,绝对是超三星标准，地处商业区，购物还是很方便的，对门有家羊杂店，绝对正宗。除了价格稍贵，总体还是很满意的
1,前台服务较差，不为客户着想。房间有朋友来需要打扫，呼叫了两个小时也未打扫。房间下水道臭气熏天，卫生间漏水堵水。
0,标准间太差房间还不如3星的而且设施非常陈旧.建议酒店把老的标准间从新改善.
0,服务态度极其差，前台接待好象没有受过培训，连基本的礼貌都不懂，竟然同时接待几个客人；大堂副理更差，跟客人辩解个没完，要总经理的电话投诉竟然都不敢给。要是没有作什么亏心事情，跟本不用这么怕。
0,地理位置还不错，到哪里都比较方便，但是服务不象是豪生集团管理的，比较差。下午睡了一觉并洗了一个澡，本来想让酒店再来打扫一下，所以，打开了，请打扫的服务灯，可是到晚上回酒店，发现打扫得服务灯被关掉了，而房间还是没有打扫过。

from tqdm import tqdm
import torch
from torch.utils.data import DataLoader, TensorDataset
import random
from NEZHA.model_nezha import BertConfig, BertForSequenceClassification
from NEZHA import nezha_utils
import numpy as np
from transformers import BertTokenizer, AdamW, AutoModel, AutoTokenizer, AutoModelForSequenceClassification


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
epochs = 1

seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)


if __name__ == '__main__':
    # tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-roberta-wwm-ext")
    tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path='NEZHA/nezha-base-wwm', do_lower_case=True)

    data = []
    with open('data.txt', 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            label, text = int(line[0]), line[2:]
            text = tokenizer.encode_plus(text, max_length=128, padding='max_length', truncation=True)
            data.append((text, label))
    random.shuffle(data)
    train_data = data[:int(len(data)*0.8)]
    test_data = data[len(train_data):]

    input_ids_train = torch.LongTensor([each[0]['input_ids'] for each in train_data]).to(device)
    token_type_ids_train = torch.LongTensor([each[0]['token_type_ids'] for each in train_data]).to(device)
    attention_mask_train = torch.LongTensor([each[0]['attention_mask'] for each in train_data]).to(device)
    label_train = torch.LongTensor([each[1] for each in train_data]).to(device)

    input_ids_test = torch.LongTensor([each[0]['input_ids'] for each in test_data]).to(device)
    token_type_ids_test = torch.LongTensor([each[0]['token_type_ids'] for each in test_data]).to(device)
    attention_mask_test = torch.LongTensor([each[0]['attention_mask'] for each in test_data]).to(device)
    label_test = torch.LongTensor([each[1] for each in test_data]).to(device)

    train_dataset = TensorDataset(input_ids_train, token_type_ids_train, attention_mask_train, label_train)
    test_dataset = TensorDataset(input_ids_test, token_type_ids_test, attention_mask_test, label_test)

    train_loader = DataLoader(dataset=train_dataset, batch_size=8, shuffle=True)
    test_loader = DataLoader(dataset=test_dataset, batch_size=8, shuffle=False)

    Bert_config = BertConfig.from_json_file('NEZHA/nezha-base-wwm/bert_config.json')
    model = BertForSequenceClassification(config=Bert_config, num_labels=2)
    nezha_utils.torch_init_model(model, 'NEZHA/nezha-base-wwm/pytorch_model.bin')

    # model = AutoModelForSequenceClassification.from_pretrained("hfl/chinese-roberta-wwm-ext")
    model.to(device)
    optimizer = AdamW(model.parameters(), lr=2e-5)

    for epoch in range(epochs):
        print('epoch:', epoch)
        model.train()
        for input_ids, token_type_ids, attention_mask, labels in tqdm(train_loader):
            optimizer.zero_grad()
            # loss = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels).loss
            loss = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels)
            loss.backward()
            optimizer.step()

        model.eval()
        total = 0
        acc = 0
        with torch.no_grad():
            for input_ids, token_type_ids, attention_mask, labels in tqdm(test_loader):
                # logits = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask).logits
                logits = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
                pred = torch.argmax(logits, dim=-1)
                total += pred.size(0)
                acc += pred.eq(labels).sum().item()
        print(acc / total)