metadata

configs:
  - config_name: MASSIVE
    data_files:
      - split: train
        path: MASSIVE/train-*
      - split: validation
        path: MASSIVE/validation-*
      - split: test
        path: MASSIVE/test-*
  - config_name: ag_news
    data_files:
      - path: ag_news/train.csv
        split: train
      - path: ag_news/validation.csv
        split: validation
      - path: ag_news/test.csv
        split: test
  - config_name: amazon_polarity
    data_files:
      - path: amazon_polarity/train.csv
        split: train
      - path: amazon_polarity/validation.csv
        split: validation
      - path: amazon_polarity/test.csv
        split: test
  - config_name: banking77
    data_files:
      - split: train
        path: banking77/train-*
      - split: test
        path: banking77/test-*
      - split: validation
        path: banking77/validation-*
  - config_name: emotion
    data_files:
      - path: emotion/train.csv
        split: train
      - path: emotion/validation.csv
        split: validation
      - path: emotion/test.csv
        split: test
  - config_name: imdb
    data_files:
      - path: imdb/train.csv
        split: train
      - path: imdb/validation.csv
        split: validation
      - path: imdb/test.csv
        split: test
  - config_name: trec
    data_files:
      - split: train
        path: trec/train-*
      - split: test
        path: trec/test-*
      - split: validation
        path: trec/validation-*
  - config_name: twenty_newsgroups
    data_files:
      - path: twenty_newsgroups/train.csv
        split: train
      - path: twenty_newsgroups/validation.csv
        split: validation
      - path: twenty_newsgroups/test.csv
        split: test
  - config_name: yelp_polarity
    data_files:
      - path: yelp_polarity/train.csv
        split: train
      - path: yelp_polarity/validation.csv
        split: validation
      - path: yelp_polarity/test.csv
        split: test
dataset_info:
  - config_name: MASSIVE
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 541604
        num_examples: 11514
      - name: validation
        num_bytes: 95125
        num_examples: 2033
      - name: test
        num_bytes: 138473
        num_examples: 2974
    download_size: 379162
    dataset_size: 775202
  - config_name: ag_news
    features:
      - dtype: string
        name: text
      - dtype: int64
        name: label
    splits:
      - name: train
        num_examples: 90000
      - name: validation
        num_examples: 30000
      - name: test
        num_examples: 7600
  - config_name: amazon_polarity
    features:
      - dtype: string
        name: text
      - dtype: int64
        name: label
    splits:
      - name: train
        num_examples: 2700000
      - name: validation
        num_examples: 900000
      - name: test
        num_examples: 400000
  - config_name: banking77
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 535404
        num_examples: 7502
      - name: test
        num_bytes: 204010
        num_examples: 3080
      - name: validation
        num_bytes: 179624
        num_examples: 2501
    download_size: 452800
    dataset_size: 919038
  - config_name: emotion
    features:
      - dtype: string
        name: text
      - dtype: int64
        name: label
    splits:
      - name: train
        num_examples: 250085
      - name: validation
        num_examples: 83362
      - name: test
        num_examples: 41681
  - config_name: imdb
    features:
      - dtype: string
        name: text
      - dtype: int64
        name: label
    splits:
      - name: train
        num_examples: 18750
      - name: validation
        num_examples: 6250
      - name: test
        num_examples: 25000
  - config_name: trec
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 255609
        num_examples: 4089
      - name: test
        num_bytes: 23979
        num_examples: 500
      - name: validation
        num_bytes: 85861
        num_examples: 1363
    download_size: 219283
    dataset_size: 365449
  - config_name: twenty_newsgroups
    features:
      - dtype: string
        name: text
      - dtype: int64
        name: label
    splits:
      - name: train
        num_examples: 8485
      - name: validation
        num_examples: 2829
      - name: test
        num_examples: 7532
  - config_name: yelp_polarity
    features:
      - dtype: string
        name: text
      - dtype: int64
        name: label
    splits:
      - name: train
        num_examples: 420000
      - name: validation
        num_examples: 140000
      - name: test
        num_examples: 38000
language:
  - en
license: apache-2.0
size_categories:
  - 1K<n<10K
  - 10K<n<100K
tags:
  - data-preprocessing
  - automl
  - quality-issues
  - benchmarks
task_categories:
  - text-classification

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

Label Noise Issues

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

Outlier Issues

emotion: Twitter emotion with length outliers

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"]

Dataset Details

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}
}