帮我写一个使用预训练模型Roberta-wwm-ext对微博通用数据进行情感分类六种情感分别为积极、愤怒、悲伤、恐惧、惊奇、无情绪的模型代码
时间: 2023-03-09 15:32:00 浏览: 97
我可以给你一个参考:from transformers import BertForSequenceClassification, AdamW, BertConfig
import torch# 构建模型
config = BertConfig.from_pretrained('roberta-wwm-ext')
model = BertForSequenceClassification.from_pretrained('roberta-wwm-ext', config=config)# 设置优化器
optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)# 设置训练的目标
num_labels = 6
labels = ['积极','愤怒','悲伤','恐惧','惊奇','无情绪']# 训练模型
model.train()
for _ in range(epochs):
# 迭代训练
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
# 将数据放入GPU
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
# 清空梯度
optimizer.zero_grad()
# 运行模型
outputs = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask, labels=b_labels)
loss = outputs[0]
logits = outputs[1]
# 反向传播
loss.backward()
# 更新参数
optimizer.step()
# 累加损失
tr_loss += loss.item()
nb_tr_examples += b_input_ids.size(0)
nb_tr_steps += 1
print("Train loss: {}".format(tr_loss/nb_tr_steps))# 模型预测
model.eval()
preds = []
for _, batch in enumerate(test_dataloader):
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
outputs = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask, labels=b_labels)
logits = outputs[1]
logits = logits.detach().cpu().numpy()
preds.append(logits)# 将预测结果转换为情感标签
pred_labels_list = []
for pred in preds:
pred_label = np.argmax(pred, axis=1).tolist()
pred_labels_list.append([labels[i] for i in pred_label])