Nlp 如何计算BERT中多类分类的所有召回准确率和f1度量？

需要计算多类模型的分类报告，但它只提供准确性和f1分数

我想您使用的是Pytorch环境。以下是打印数据集中每个类的F1、回调和精度的正确代码。如果您有一个经过训练的模型，请加载它以及要测试的数据集

from sklearn.metrics import f1_score

def f1_score_func(preds, labels):
    preds_flat = np.argmax(preds, axis=1).flatten()
    labels_flat = labels.flatten()
    return f1_score(labels_flat, preds_flat, average='weighted')

def accuracy_per_class(preds, labels):
    label_dict_inverse = {v: k for k, v in label_dict.items()}
    
    preds_flat = np.argmax(preds, axis=1).flatten()
    labels_flat = labels.flatten()

    for label in np.unique(labels_flat):
        y_preds = preds_flat[labels_flat==label]
        y_true = labels_flat[labels_flat==label]
        print(f'Class: {label_dict_inverse[label]}')
        print(f'Accuracy: {len(y_preds[y_preds==label])}/{len(y_true)}\n')

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我不太明白你的问题。你试过了吗？是的，没错，我还需要显示混淆矩阵。sklearn的分类报告有什么问题？它输出召回率、精确性和f-scoresIt的文本分类问题，其中包含10个多类。您实际尝试过使用sklearns的分类报告吗？输出是什么？有什么错误吗？你的问题不清楚，需要澄清一下。

from sklearn.metrics import classification_report, confusion_matrix

val_dataset = LoadDataset('/content/val.csv')
val_loader = torch.utils.data.DataLoader(val_dataset,batch_size=51) # Load the data

model.load_state_dict(torch.load('vit-base.bin')) # Load the trained model
model.cuda()                                      # For putting model on GPUs
with torch.no_grad():
 image,target = next(iter(val_loader))
 image = image.to(device)
 target = target.flatten().to(device)
 prediction = model(image)

prediction = prediction.argmax(dim=1).view(target.size()).cpu().numpy()
target = target.cpu().numpy()
print(classification_report(target,prediction,target_names=val_dataset.LE.classes_)) # LE is the label encoder