该BertCrf的训练代码
时间: 2024-03-21 19:38:06 浏览: 36
以下是使用BERT和CRF进行中文命名实体识别任务训练的PyTorch代码示例:
```python
import torch
from torch.utils.data import DataLoader
from transformers import BertTokenizer
from seqeval.metrics import f1_score
from dataset import NerDataset
from model import BertCrf
# 加载训练集和验证集
train_dataset = NerDataset('train.txt', tokenizer)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
val_dataset = NerDataset('val.txt', tokenizer)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
# 初始化模型和优化器
model = BertCrf(num_tags=len(label2id))
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
# 训练模型
for epoch in range(10):
model.train()
train_loss = 0
for input_ids, attention_mask, labels in train_loader:
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
labels = labels.to(device)
loss = model(input_ids, attention_mask, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_loss = train_loss / len(train_loader)
print('Epoch: {0}, Train Loss: {1:.4f}'.format(epoch, train_loss))
# 验证模型
model.eval()
val_loss = 0
val_preds = []
val_labels = []
with torch.no_grad():
for input_ids, attention_mask, labels in val_loader:
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
labels = labels.to(device)
loss = model(input_ids, attention_mask, labels)
val_loss += loss.item()
preds = model(input_ids, attention_mask)
val_preds.extend(preds)
val_labels.extend(labels.tolist())
val_loss = val_loss / len(val_loader)
val_f1 = f1_score(val_labels, val_preds)
print('Epoch: {0}, Val Loss: {1:.4f}, Val F1: {2:.4f}'.format(epoch, val_loss, val_f1))
```
在上面的代码中,我们使用`BertTokenizer`加载BERT预训练模型的分词器,并使用`NerDataset`加载数据集。然后我们初始化BertCrf模型和AdamW优化器,并在训练集上训练模型。在每个epoch结束时,我们使用验证集计算模型的损失和F1分数,并打印出结果。
希望这个代码示例对你有所帮助。