Create README.md

README.md

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+---
+task_categories:
+- tabular-classification
+- tabular-regression
+language:
+- en
+tags:
+- tabular
+- benchmark
+- strings
+- heterogeneous
+size_categories:
+- 100K<n<1M
+---
+
+# STRABLE: Benchmarking Tabular Machine Learning with Strings
+
+STRABLE is a benchmarking corpus of **108 real-world tabular datasets** containing string features,
+designed to support empirical research on tabular machine learning pipelines that handle string entries.
+
+## Dataset Summary
+
+Existing tabular benchmarks either exclude string columns or flatten them into fixed numerical
+representations before evaluation, preventing the study of alternative string-handling strategies.
+STRABLE fills this gap with raw, minimally preprocessed tables spanning 8 application fields,
+covering 13 binary classification, 19 multi-class classification, and 76 regression tasks.
+
+## Key Statistics
+
+- **108 datasets** from 33 distinct public sources
+- **8 application fields**: Economy, Health, Infrastructure, Energy, Education, Commerce, Food, Social
+- Median: 7,700 rows · 18 columns · 17-character strings per cell · 1,200 unique values per string column
+- **String taxonomy**: Categorical (50%), Names (23%), Structured Codes (17%), Free Text (8%), Identifiers (0.5%)
+
+## Loading a Dataset
+
+Each of the 108 datasets is stored as a parquet file in its own subdirectory.
+
+```python
+import pandas as pd
+
+# Load one dataset directly
+df = pd.read_parquet(
+    "hf://datasets/strablebench/STRABLE/beer-ratings/data.parquet"
+)
+
+# Or via the HuggingFace datasets library
+from datasets import load_dataset
+ds = load_dataset("strablebench/STRABLE", data_dir="beer-ratings")
+df = ds["train"].to_pandas()
+```
+
+Available dataset names (subdirectory keys) are listed in the repository file tree,
+and described in full in Appendix C.4 of the paper.
+
+## String Taxonomy
+
+| Type | Description | Example |
+|---|---|---|
+| Categorical | Low-uniqueness repeating labels | "General Acute Care", "Red" |
+| Name | Proper nouns for entities | "John Doe", "Max Mara" |
+| Structured Code | Strings with recognizable patterns | ZIP codes, ICD codes, dates |
+| Free Text | Multi-word natural language prose | User reviews, clinical notes |
+| Identifier | Near-unique opaque keys | UUIDs, auto-generated IDs |
+
+## Preprocessing
+
+STRABLE applies minimal preprocessing to reflect data as found in practice:
+- Flattening of nested structures; removal of duplicate rows, single-value columns, all-null columns
+- No feature engineering; no missing values imputation
+- Regression targets skewness-minimized via a candidate set of transformations
+- Large tables subsampled to 75,000 rows (stratified for classification)
+
+## Citation
+
+```bibtex
+@inproceedings{strable2026neurips,
+  title     = {{STRABLE}: Benchmarking Tabular Machine Learning with Strings},
+  booktitle = {Anonimous Conference},
+  year      = {2026},
+}
+```
+
+## License
+
+Individual source datasets retain their original licenses;
+see Appendix C.4 of the paper for per-dataset attribution and source URLs.