| ag_news: |
| class_names: |
| - World |
| - Sports |
| - Business |
| - Technology |
| description: News categorization with 4 classes, known for similar content across |
| categories |
| name: AG News Classification |
| num_classes: 4 |
| original_test_samples: 7600 |
| original_train_samples: 120000 |
| quality_issues: |
| - redundancy |
| - similar_content |
| - topic_overlap |
| target_column: label |
| task_type: multi_classification |
| test_samples: 7600 |
| text_columns: |
| - text |
| total_samples: 127600 |
| train_samples: 90000 |
| validation_samples: 30000 |
| amazon_polarity: |
| class_names: |
| - negative |
| - positive |
| description: Amazon reviews with noisy sentiment labels |
| name: Amazon Product Reviews |
| num_classes: 2 |
| original_test_samples: 400000 |
| original_train_samples: 3600000 |
| quality_issues: |
| - label_noise |
| - rating_inconsistency |
| target_column: label |
| task_type: binary_classification |
| test_samples: 400000 |
| text_columns: |
| - text |
| total_samples: 4000000 |
| train_samples: 2700000 |
| validation_samples: 900000 |
| emotion: |
| class_names: |
| - sadness |
| - joy |
| - love |
| - anger |
| - fear |
| - surprise |
| description: Twitter emotion classification with text length outliers |
| name: Emotion Classification |
| num_classes: 6 |
| original_test_samples: 41681 |
| original_train_samples: 333447 |
| quality_issues: |
| - length_outliers |
| - text_anomalies |
| target_column: label |
| task_type: multi_classification |
| test_samples: 41681 |
| text_columns: |
| - text |
| total_samples: 375128 |
| train_samples: 250085 |
| validation_samples: 83362 |
| imdb: |
| class_names: |
| - negative |
| - positive |
| description: Movie reviews with subjective sentiment labels and borderline cases |
| name: IMDB Movie Reviews |
| num_classes: 2 |
| original_test_samples: 25000 |
| original_train_samples: 25000 |
| quality_issues: |
| - label_noise |
| - subjective_labels |
| - borderline_cases |
| target_column: label |
| task_type: binary_classification |
| test_samples: 25000 |
| text_columns: |
| - text |
| total_samples: 50000 |
| train_samples: 18750 |
| validation_samples: 6250 |
| twenty_newsgroups: |
| class_names: |
| - alt.atheism |
| - comp.graphics |
| - comp.os.ms-windows.misc |
| - comp.sys.ibm.pc.hardware |
| - comp.sys.mac.hardware |
| - comp.windows.x |
| - misc.forsale |
| - rec.autos |
| - rec.motorcycles |
| - rec.sport.baseball |
| - rec.sport.hockey |
| - sci.crypt |
| - sci.electronics |
| - sci.med |
| - sci.space |
| - soc.religion.christian |
| - talk.politics.guns |
| - talk.politics.mideast |
| - talk.politics.misc |
| - talk.religion.misc |
| description: Newsgroup posts with overlapping topics and cross-posting |
| name: 20 Newsgroups |
| num_classes: 20 |
| original_test_samples: 7532 |
| original_train_samples: 11314 |
| quality_issues: |
| - redundancy |
| - cross_posting |
| - similar_topics |
| target_column: label |
| task_type: multi_classification |
| test_samples: 7532 |
| text_columns: |
| - text |
| total_samples: 18846 |
| train_samples: 8485 |
| validation_samples: 2829 |
| yelp_polarity: |
| class_names: |
| - negative |
| - positive |
| description: Yelp reviews with positive/negative sentiment, naturally imbalanced |
| name: Yelp Review Polarity |
| num_classes: 2 |
| original_test_samples: 38000 |
| original_train_samples: 560000 |
| quality_issues: |
| - moderate_imbalance |
| - rating_bias |
| target_column: label |
| task_type: binary_classification |
| test_samples: 38000 |
| text_columns: |
| - text |
| total_samples: 598000 |
| train_samples: 420000 |
| validation_samples: 140000 |
|
|
