基本只需要一行代码,即可找出数据集中的哪些示例存在问题: fromcleanlab.classificationimportCleanLearningissues = CleanLearning(yourFavoriteModel).find_label_issues(data, labels) 一行代码,就能衡量和跟踪数据集的整体健康状况: fromcleanlab.datasetimportoverall_label_health_scorehealth = overall_label_health_score(...
引入Cleanlab 接下来,我们引入Cleanlab并进行标签错误检测。 fromcleanlab.classificationimportCleanLearning fromcleanlab.datasetimportfind_label_issues # 创建CleanLearning对象 clean_learning=CleanLearning() # 识别标签错误 label_issues=find_label_issues(X,y) # 输出标签错误的索引 print("标签错误的索引:",label...
from cleanlab.classificationimportCleanLearningissues=CleanLearning(yourFavoriteModel).find_label_issues(data,labels) 一行代码,就能衡量和跟踪数据集的整体健康状况: 代码语言:javascript 复制 from cleanlab.datasetimportoverall_label_health_scorehealth=overall_label_health_score(labels,pred_probs) 此外,cleanlab ...
from cleanlab.classification import CleanLearningissues = CleanLearning(yourFavoriteModel).find_label_issues(data, labels) 一行代码,就能衡量和跟踪数据集的整体健康状况: from cleanlab.dataset import overall_label_health_scorehealth = overall_label_health_score(labels, pred_probs) 此外,cleanlab 的所有功能...
在这个示例中,我们首先加载了鸢尾花数据集,并使用Cleanlab的find_label_issues函数 来识别数据集中的标签错误。然后,我们使用CleanLearning对象的fit_predict方法来修复这些错误的标签。 最后,我们输出了修复前后的标签进行对比。 四、Cleanlab的优势 易于使用:Cleanlab提供了易于使用的接口和丰富的功能,使得数据清洗过程更加...
find_label_issues(data, labels) 一行代码衡量并跟踪数据集整体健康状况: from cleanlab.dataset import overall_label_health_score # pred_probs = 样本外的预测概率, 通过交叉验证获得dataset_health = overall_label_health_score(labels, pred_probs) 官方公告博客(更多详情):cleanlab.ai/blog/cleanl GitHub:...
cleanlab: Find Label Errors in ImageNetUse cleanlab to identify ~100,000 label errors in the 2012 ImageNet training dataset.Top label issues in the 2012 ILSVRC ImageNet train set identified using cleanlab. Label Errors are boxed in red. Ontological issues in green. Multi-label images in blue...
Top label issues in the 2012 ILSVRC ImageNet train set identified using cleanlab. Label Errors are boxed in red. Ontological issues in green. Multi-label images in blue.cleanlab: Find Label Errors in MNISTUse cleanlab to identify ~50 label errors in the MNIST dataset....
(train_data,labels)# 识别错误标签的示例cl.fit(train_data,labels,label_issues=label_issues)preds=cl.predict(test_data)# 从通过自动清理的数据训练过后的模型进行预测fromcleanlab.filterimportfind_label_issuesranked_label_issues=find_label_issues(labels,pred_probs,return_indices_ranked_by="self...
# Finding label issues in the train set label_issues = cl.find_label_issues(X=train_texts, labels=train_labels) # Picking top 50 samples based on confidence scores identified_issues = label_issues[label_issues["is_label_issue"] == True] lowest_quality_labels = label_issues["label_quality...