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README.md

@@ -1,3 +1,232 @@
----
-license: apache-2.0
----
+---
+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
+
+```python
+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:
+
+```bibtex
+@misc{mothmalone2024preprocessing,
+  title={Data Preprocessing AutoML Benchmarks},
+  author={MothMalone},
+  year={2024},
+  url={https://huggingface.co/datasets/MothMalone/data-preprocessing-automl-benchmarks}
+}
+```