bert模型用于二分类问题微调_bert base chinese 二分类

使用bert-base-chinese预训练模型对二分类问题进行微调

import pandas as pd
from transformers import BertTokenizerFast, AutoModelForSequenceClassification, Trainer, TrainingArguments
import torch

model_name = "./bert-base-chinese"
path = "./abuse_22.csv"


df = pd.read_csv(path, encoding="utf-8")
texts = df["content"][:1000].tolist()
labels = df["punish_result"][:1000].tolist()
texts = list(map(lambda x: str(x), texts))

class Dataset(torch.utils.data.Dataset):

    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.labels)
    

model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
tokenizer = BertTokenizerFast.from_pretrained(model_name)

# 参考这里 https://blog.csdn.net/weixin_42924890/article/details/139269528
train_encodings = tokenizer(texts, truncation=True, padding=True, max_length=512)
encodings = Dataset(train_encodings, labels)


args = TrainingArguments(output_dir='./output_dir',
                         evaluation_strategy='epoch',
                         no_cuda=True,
                         num_train_epochs=2,
                         learning_rate=1e-4,
                         weight_decay=1e-2,
                         per_device_eval_batch_size=32,
                         per_device_train_batch_size=32)


trainer = Trainer(
    model=model,
    args=args,
    train_dataset=encodings,
)

# 开始训练
trainer.train()
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