config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput...
config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput...
_ = get_multilabel_classification_dataset()candidate_labels = [ "Quality", "Price", "Delivery", "Service", "Product Variety", "Customer Support", "Packaging", "User Experience", "Return Policy", "Product Information"]clf = MultiLabelZero...
创建MultiLabelZeroShotGPTClassifier类的实例时，指定要分配给每个样本的最大标签数量（这里:max_labels=3）数据没有没有标签怎么办？可以通过提供候选标签列表来训练没有标记数据的分类器。y的类型应该是List[List[str]]。下面是一个没有标记数据的训练示例:# getting classification dataset for prediction onlyX, _...
X, _=get_multilabel_classification_dataset() # Defining all the labels that needs to predicted candidate_labels= [ "Quality", "Price", "Delivery", "Service", "Product Variety" ] # creating the model clf=MultiLabelZeroShotGPTClassifier(max_labels=3) ...
X, _ = get_multilabel_classification_dataset() # Defining all the labels that needs to predicted candidate_labels = [ "Quality", "Price", "Delivery", "Service", "Product Variety" ] # creating the model clf = MultiLabelZeroShotGPTClassifier(max_labels=3) ...
X, _ = get_multilabel_classification_dataset() # Defining all the labels that needs to predicted candidate_labels = [ "Quality", "Price", "Delivery", "Service", "Product Variety" ] # creating the model clf = MultiLabelZeroShotGPTClassifier(max_labels=3) ...
MultiLabelZeroShotGPTClassifier 多标签也类似 # importing Multi-Label zeroshot module and classification dataset fromskllmimportMultiLabelZeroShotGPTClassifier fromskllm.datasetsimportget_multilabel_classification_dataset # get classification dataset from sklearn ...
MultiLabelZeroShotGPTClassifier 多标签也类似 # importing Multi-Label zeroshot module and classification dataset fromskllmimportMultiLabelZeroShotGPTClassifier fromskllm.datasetsimportget_multilabel_classification_dataset # get classification dataset from sklearn X, y = get_multilabel_classification_dataset #...
For the sake of simplicity, we’ll focus solely on positive and negative sentiments, but the process can be easily extended to handle multi-label classification. A sample of the dataset containing financial and economic news. Data access and prompt engineering We start off by importing...