解决报错“OSError: We couldn‘t connect to ‘https://huggingface.co‘ to load this file”

今天在于Bert进行文本情感分析时，由于要调用Bert的预训练模型，但是出现报错“OSError: We couldn‘t connect to ‘https://huggingface.co‘ to load this file”

意思是无法访问这个网址，点进这个网址，浏览器访问，依旧失败

原因是这个网址，国内的IP已经没办法访问了。 于是想到用离线模式下载

方法一：安全上网，访问该网址

方法二：使用镜像网址

非常好用的镜像网站：

HF-Mirror - Huggingface 镜像站

不需要安全上网也能访问！

 接下来是解决方法：

1、搜索 bert-base-uncased

2、下载所需要的文件（权重文件）

3、将下载的文件保存到项目下面

4、更改源代码中的地址

主要是在预训练模块处进行更改

5、成功运行！

附上Bert的一个简单运行demo

import pickle
 
import torch
from transformers import BertTokenizer, BertForSequenceClassification
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader
 
# 读取数据
pickle_file = 'data/Belt_and_Road.pickle'
with open(pickle_file, 'rb') as f:
    pickle_data = pickle.load(f)  # 反序列化，与pickle.dump相反
    X_train = pickle_data['train_dataset']
    y_train = pickle_data['train_labels']
    X_val = pickle_data['test_dataset']
    y_val = pickle_data['test_labels']
    del pickle_data  # 释放内存
 
print('Data and modules loaded.')
 
# 加载预训练的BERT模型和tokenizer
bert_model_path = 'uncased'
tokenizer = BertTokenizer.from_pretrained(bert_model_path)
model = BertForSequenceClassification.from_pretrained(bert_model_path, num_labels=3)  # 3 是输出类别数
 
# 对文本进行tokenize和padding
max_length = 128
train_encodings = tokenizer(X_train, truncation=True, padding=True, max_length=max_length)
val_encodings = tokenizer(X_val, truncation=True, padding=True, max_length=max_length)
 
# 转换为PyTorch张量
train_dataset = TensorDataset(torch.tensor(train_encodings['input_ids']),
                              torch.tensor(train_encodings['attention_mask']),
                              torch.tensor(y_train))
val_dataset = TensorDataset(torch.tensor(val_encodings['input_ids']),
                            torch.tensor(val_encodings['attention_mask']),
                            torch.tensor(y_val))
 
# 定义数据加载器
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
 
# 定义优化器和损失函数
optimizer = torch.optim.Adam(model.parameters(), lr=2e-5)
criterion = torch.nn.CrossEntropyLoss()
 
# 训练模型
num_epochs = 3
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for input_ids, attention_mask, labels in train_loader:
        input_ids, attention_mask, labels = input_ids.to(device), attention_mask.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
 
    # 在验证集上评估模型
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for input_ids, attention_mask, labels in val_loader:
            input_ids, attention_mask, labels = input_ids.to(device), attention_mask.to(device), labels.to(device)
            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
            _, predicted = torch.max(outputs.logits, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
 
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}, Validation Accuracy: {correct/total:.2%}')

都看到这里了~给个小心心❤❤呗~