NER_ZH运行问题

Question

在使用您发布在GitCode上的项目https://gitcode.com/tmylla/NER_ZH%E6%97%B6%E4%BB%A3%E7%A0%81%E8%BF%90%E8%A1%8C%E5%87%BA%E7%8E%B0%E9%97%AE%E9%A2%98%EF%BC%8Cckpts%E6%96%87%E4%BB%B6%E5%A4%B9%E4%B8%8B%E4%B8%A4%E4%B8%AA%E6%A8%A1%E5%9E%8B%E5%9D%87%E4%B8%BAbert_base_chinse,tokenizer%E8%BF%90%E8%A1%8C%E6%97%A0%E8%AF%AF%EF%BC%8Cinput_ids, token_type_ids, attention_mask检查后无误，导入模型后outputs张量全为nan值，预测结果均为同一标签，请问作者如何解决。
所有的包均已安装，torch版本与cuda一致。

```python
NaN values found in outputs
BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]],

        [[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]],

        [[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]],

        ...,

        [[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]],

        [[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]],

        [[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]]], device='cuda:0'), pooler_output=tensor([[nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        ...,
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan],
        [nan, nan, nan,  ..., nan, nan, nan]], device='cuda:0'), hidden_states=None, past_key_values=None, attentions=None, cross_attentions=None)

![img](https://img-mid.csdnimg.cn/release/static/image/mid/ask/57e4824744db4070b938859e192466d1.png "#left")

Answer 1

引自免费微信小程序：皆我百晓生

看起来你在尝试使用BERT进行命名实体识别（NER），但是遇到了输出全是nan的问题。这种情况通常是由于梯度爆炸或者梯度消失导致的。这里有一些可能的解决方案：

1. 检查输入数据格式: 确保你的输入数据已经正确地经过了分词、编码，并且形状和大小是正确的。例如，input_ids, token_type_ids, 和 attention_mask 应该符合BERT模型的预期。

2. 检查数据规模: 如果数据集中存在特别长的句子，这可能导致梯度爆炸。尝试对句子进行截断，限制每个样本的最大长度。

3. 初始化权重: 尽管BERT通常不需要特殊的权重初始化，但如果你是从随机权重开始训练，确保你的学习率设置得足够小。

4. 学习率调整: 使用较小的学习率，比如1e-5到5e-5之间。可以尝试使用学习率调度器，例如BERT推荐的warmup策略。

5. 检查模型加载: 确保模型和预训练权重都已经正确加载。如果模型没有加载成功，可能会得到无效的输出。

6. 检查GPU内存: 确保你的GPU有足够的内存来处理你的数据和模型。如果内存不足，可能会导致错误的结果。

7. 启用mixed precision training: 在某些情况下，使用混合精度训练（Mixed Precision）可以帮助缓解梯度问题并提高性能。你可以使用PyTorch的apex.amp模块来进行此操作。

下面是一个简化版的示例代码，展示了如何使用BERT进行NER以及一些上述建议的实践：

import torch
from transformers import BertTokenizerFast, BertForTokenClassification, AdamW
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
import numpy as np

# 假设你有一个包含输入文本和对应标签的数据集
class NERDataset(Dataset):
    # 实现__len__, __getitem__等方法

# 划分训练集和验证集
train_dataset, val_dataset = train_test_split(dataset, test_size=0.2)

# 初始化 tokenizer 和 model
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = BertForTokenClassification.from_pretrained('bert-base-chinese')

# 数据加载器
batch_size = 8
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)

# 模型参数和优化器
params = list(model.parameters())
optimizer = AdamW(params, lr=5e-5)

# 开始训练
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

num_epochs = 3
for epoch in range(num_epochs):
    for step, (inputs, labels) in enumerate(train_dataloader):
        inputs = {key: value.to(device) for key, value in inputs.items()}
        labels = labels.to(device)

        # Forward pass
        outputs = model(**inputs, labels=labels)
        loss = outputs.loss

        # Backward pass
        optimizer.zero_grad()
        loss.backward()

        # Clip gradients to avoid exploding gradients
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

        # Update weights
        optimizer.step()

    # Validation after each epoch
    with torch.no_grad():
        total_loss = 0
        for inputs, labels in val_dataloader:
            inputs = {key: value.to(device) for key, value in inputs.items()}
            labels = labels.to(device)

            outputs = model(**inputs, labels=labels)
            loss = outputs.loss
            total_loss += loss.item()

        avg_val_loss = total_loss / len(val_dataloader)

        print(f'Epoch {epoch + 1}/{num_epochs}, Validation Loss: {avg_val_loss:.4f}')

# Save the trained model
torch.save(model.state_dict(), 'ner_model.pth')

这个代码片段展示了一个简单的BERT NER训练流程，包括数据预处理、模型加载、训练循环、损失计算和模型保存。你需要根据你的实际数据集替换NERDataset类中的实现部分。

如果你仍然遇到问题，请提供更多的上下文信息，如数据样例、完整的代码段，以及任何其他可能有用的调试信息，这样我可以更具体地帮助你。