README.md

metadata

license: apache-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - data-preprocessing
  - automl
  - quality-issues
  - benchmarks
size_categories:
  - 1K<n<10K
  - 10K<n<100K

Data Preprocessing AutoML Benchmarks

This repository contains text classification datasets with known data quality issues for preprocessing research in AutoML.

Dataset Categories

Redundancy Issues

• ag_news: News categorization with topic overlap
• twenty_newsgroups: Newsgroup posts with cross-posting

Class Imbalance Issues

• yelp_polarity: Sentiment analysis with rating bias
• sms_spam: Spam detection with severe imbalance

Label Noise Issues

• imdb: Movie reviews with subjective labels
• amazon_polarity: Product reviews with rating inconsistencies

Outlier Issues

• emotion: Twitter emotion with length outliers
• financial_phrasebank: Financial sentiment with domain outliers

Clean Baselines

• trec: Question classification with clean labels

Dataset Structure

Each dataset contains:

• train.csv: Training split (~75% of original training data)
• validation.csv: Validation split (~25% of original training data)
• test.csv: Test split (original test set preserved)

All datasets have consistent columns:

• text: Input text
• label: Target label (integer encoded)

Important: Original test sets are preserved to maintain methodological integrity and enable comparison with published benchmarks.

Usage

from datasets import load_dataset

# Load a specific dataset
dataset = load_dataset("MothMalone/data-preprocessing-automl-benchmarks", "ag_news")

# Access splits
train_data = dataset["train"]
val_data = dataset["validation"] 
test_data = dataset["test"]

Metadata

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

Citation

If you use these datasets in your research, please cite the original sources and this collection:

@misc{mothmalone2024preprocessing,
  title={Data Preprocessing AutoML Benchmarks},
  author={MothMalone},
  year={2024},
  url={https://huggingface.co/datasets/MothMalone/data-preprocessing-automl-benchmarks}
}