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README.md

@@ -1,122 +1,19 @@
 ---
-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
+license: apache-2.0
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- data-preprocessing
+- automl
+- benchmarks
+size_categories:
+- n<1K
+- 1K<n<10K
+- 10K<n<100K
+- 100K<n<1M
 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
@@ -125,42 +22,11 @@ dataset_info:
     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
@@ -169,265 +35,118 @@ dataset_info:
     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
+- config_name: financial_phrasebank
   features:
-  - dtype: string
-    name: text
-  - dtype: int64
-    name: label
+  - name: text
+    dtype: string
+  - name: label
+    dtype: int64
   splits:
   - name: train
-    num_examples: 8485
-  - name: validation
-    num_examples: 2829
+    num_examples: 1698
   - name: test
-    num_examples: 7532
-- config_name: yelp_polarity
+    num_examples: 0
+  - name: validation
+    num_examples: 566
+- config_name: MASSIVE
   features:
-  - dtype: string
-    name: text
-  - dtype: int64
-    name: label
+  - name: text
+    dtype: string
+  - name: label
+    dtype: int64
   splits:
   - name: train
-    num_examples: 420000
-  - name: validation
-    num_examples: 140000
+    num_examples: 11514
   - 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
+    num_examples: 2974
+  - name: validation
+    num_examples: 2033
+configs:
+- config_name: banking77
+  data_files:
+  - split: train
+    path: data/banking77-train.parquet
+  - split: test
+    path: data/banking77-test.parquet
+  - split: validation
+    path: data/banking77-validation.parquet
+- config_name: trec
+  data_files:
+  - split: train
+    path: data/trec-train.parquet
+  - split: test
+    path: data/trec-test.parquet
+  - split: validation
+    path: data/trec-validation.parquet
+- config_name: financial_phrasebank
+  data_files:
+  - split: train
+    path: data/financial_phrasebank-train.parquet
+  - split: test
+    path: data/financial_phrasebank-test.parquet
+  - split: validation
+    path: data/financial_phrasebank-validation.parquet
+- config_name: MASSIVE
+  data_files:
+  - split: train
+    path: data/MASSIVE-train.parquet
+  - split: test
+    path: data/MASSIVE-test.parquet
+  - split: validation
+    path: data/MASSIVE-validation.parquet
 ---
-
 # 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
 
+Load a specific dataset configuration like this:
+
 ```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"]
+# Example for loading the TREC dataset
+dataset = load_dataset("MothMalone/data-preprocessing-automl-benchmarks", "trec")
 ```
 
-## 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:
-
-```bibtex
-@misc{mothmalone2024preprocessing,
-  title={Data Preprocessing AutoML Benchmarks},
-  author={MothMalone},
-  year={2024},
-  url={https://huggingface.co/datasets/MothMalone/data-preprocessing-automl-benchmarks}
-}
-```
+## Available Datasets
+
+Below are the details for each dataset configuration available in this repository.
+
+### banking77
+- Description: 
+- Data Quality Issue: N/A
+- Classes: 77
+- Training Samples: 7502
+- Validation Samples: 2501
+- Test Samples: 3080
+
+### trec
+- Description: The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in training set and another 500 for test set.
+- Data Quality Issue: N/A
+- Classes: 6
+- Training Samples: 4089
+- Validation Samples: 1363
+- Test Samples: 500
+
+### financial_phrasebank
+- Description: The key arguments for the low utilization of statistical techniques in
+- Data Quality Issue: N/A
+- Classes: 3
+- Training Samples: 1698
+- Validation Samples: 566
+- Test Samples: 0
+
+### MASSIVE
+- Description: 
+- Data Quality Issue: N/A
+- Classes: 60
+- Training Samples: 11514
+- Validation Samples: 2033
+- Test Samples: 2974