基于 chinese-roberta-wwm-ext 微调训练 6 分类情感分析模型

**基于Chinese-RoBERTa-WWM-Ext微调的6分类情感分析模型**

在本文中，我们将介绍如何使用Chinese-RoBERTa-WWM-Ext预训练语言模型，微调一个6分类情感分析模型。我们将使用PyTorch和Transformers库来实现这个任务。

**环境准备**

首先，我们需要安装必要的库：

bashpip install transformers torch

import pandas as pd# 加载数据df = pd.read_csv('data.csv')

# 将文本和情感分离text = df['text']
label = df['label']

# 将情感转换为数字标签（0-5）
label = label.astype(int)

from transformers import ChineseRoBertaTokenizer, ChineseRoBertaForSequenceClassification# 加载tokenizer和模型tokenizer = ChineseRoBertaTokenizer.from_pretrained('chinese-roberta-wwm-ext')
model = ChineseRoBertaForSequenceClassification.from_pretrained('chinese-roberta-wwm-ext', num_labels=6)

from transformers import Trainer, TrainingArguments# 定义训练参数training_args = TrainingArguments(
 output_dir='./results',
 num_train_epochs=3,
 per_device_train_batch_size=16,
 per_device_eval_batch_size=64,
 evaluation_strategy='epoch',
 learning_rate=1e-5,
 save_steps=500,
 load_best_model_at_end=True,
 metric_for_best_model='f1',
 greater_is_better=True,
 save_total_limit=2,
)

# 定义数据加载器train_dataset = ...
eval_dataset = ...

# 微调模型trainer = Trainer(
 model=model,
 args=training_args,
 train_dataloader=train_dataset,
 eval_dataloader=eval_dataset,
)
trainer.train()

#评估模型results = trainer.evaluate()
print(results)

#保存模型trainer.save_model('model.pth')