SART contains 3000 tweets labelled with respect to the polarity of the sentiment expressed: positive, negative or neutral. Each class contains 1300 tweets and the dataset is split into train/validatio

DataCite Commons2022-11-26 更新60

autoevaluate/autoeval-staging-eval-project-e4791b21-302d-4702-9dba-a4a3a73498cd-118114

文本情感分类

性能评估

该数据集包含由AutoTrain生成的模型预测结果，用于多类文本分类任务。使用的模型是autoevaluate/multi-class-classification-not-evaluated，数据集为emotion，配置为default，分割为test。评估任务的具体信息包括任务类型、模型、评估指标、数据集名称、配置、分割以及列映射。

Hugging Face2022-12-02 更新120

argilla-internal-testing/test_import_dataset_from_hub_with_classlabel_b010af28-3ae0-48b9-a125-fd69a3cc4643

文本情感分类

情感极性分析

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': positive '1': negative splits: - name: train

Hugging Face2024-09-30 更新60