参考B站BigC_666：微调BERT模型做情感分类实战，代码逐行讲解，100%可以跑通!!! 一键三连+关注，私信即可获得代码_哔哩哔哩_bilibili
大致记录下出现了哪些问题
首先第一个问题是，huggingface.co无法访问，通过修改环境变量让目标地址改到镜像地址也没起作用，最终解决方案是挂代理下载了
import os
os.environ['HTTP_PROXY'] = 'http://127.0.0.1:7890'
os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:7890'
后面就是jupyter代码了，修改了评估的部分
from datasets import load_datasetimport osimport os
os.environ['HTTP_PROXY'] = 'http://127.0.0.1:7890'
os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:7890'os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
dataset = load_dataset("imdb")
print(dataset)import datasets
print(datasets.config.HF_DATASETS_CACHE)from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
print("done")
def tokenizer_func(examples):return tokenizer(examples['text'],padding='max_length',truncation=True)
tokens_dataset = dataset.map(tokenizer_func,batched=True)
print(tokens_dataset)
print(tokens_dataset['train'][0])
train_dataset = tokens_dataset['train'].select(range(5000))
test_dataset = tokens_dataset['test'].shuffle(42).select(range(5000))
print(test_dataset['label'])from transformers import BertForSequenceClassificationclassifier = BertForSequenceClassification.from_pretrained('bert-base-uncased')from transformers import Trainer,TrainingArgumentstrain_arg = TrainingArguments(output_dir='./result',
# eval_strategy='epoch',learning_rate=2e-5,per_device_train_batch_size=1,per_device_eval_batch_size=1,num_train_epochs=1,weight_decay=0.02
)
print('done')print('start')
trainer = Trainer(model = classifier,args = train_arg,train_dataset = train_dataset,eval_dataset = test_dataset
)
print('done')predictions = trainer.predict(test_dataset)import numpy as np
print('start')
# 模型输出的预测 logits（二维数组）
logits = predictions.predictions # shape: (num_samples, num_classes)
labels = predictions.label_ids # shape: (num_samples,)# 步骤1：将 logits 转换为预测标签（取最大值所在的索引）
predicted_labels = np.argmax(logits, axis=1)# 步骤2：计算准确率
accuracy = np.mean(predicted_labels == labels)print(f"Accuracy: {accuracy:.4f}")trainer.train()# metric = trainer.evaluate()
print(test_dataset['label'])predictions = trainer.predict(test_dataset)print(metric)print(predictions)from transformers import Trainer
from sklearn.metrics import accuracy_score
import numpy as np# 1. 定义 compute_metrics
def compute_metrics(eval_pred):predictions, labels = eval_predpreds = np.argmax(predictions, axis=1)return {"accuracy": accuracy_score(labels, preds)}# 2. 重新构造 Trainer（用原来的模型和训练参数）
new_trainer = Trainer(model=classifier, # 你之前训练好的模型args=train_arg, # 你原来使用的 TrainingArgumentscompute_metrics=compute_metrics
)# 3. 调用 predict
results = new_trainer.predict(test_dataset)print("准确率：", results.metrics["test_accuracy"])
代码比较混乱，可读性比较差，实验性质，各种参数没有详细的考量
