Update README.md

README.md

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----
-license: cc-by-4.0
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+---
+license: cc-by-4.0
+tags:
+  - calibration
+  - post-hoc calibration
+  - uncertainty quantification
+  - benchmark
+  - tabular
+  - computer vision
+size_categories:
+  - 1M<n<10M
+---
+
+# CalArena — Calibration Benchmark Dataset
+
+CalArena is a large-scale benchmark for evaluating post-hoc calibration methods on classification models.
+It covers **7 benchmarks** across tabular and computer vision domains, spanning hundreds of (dataset, model) pairs and three problem types (binary, multiclass and large scale multiclass).
+
+Each entry in the benchmark is a `(p_cal, y_cal, p_test, y_test)` tuple — the calibration split and test split of predicted probabilities and ground-truth labels for one (dataset, model) pair.
+Calibration methods are fitted on the calibration split and evaluated on the test split.
+
+This dataset is the data companion to the [CalArena code repository](https://github.com/super-anonymous-researcher/CalArena).
+
+---
+
+## Files
+
+| File | Description | Size |
+|---|---|---|
+| `tabrepo-binary.h5` | Binary classification, classical tabular models | ~36 MB |
+| `tabrepo-binary-experiments.csv` | Experiment index for `tabrepo-binary` | < 1 MB |
+| `tabarena-binary.h5` | Binary classification, modern tabular foundation models | ~26 MB |
+| `tabarena-binary-experiments.csv` | Experiment index for `tabarena-binary` | < 1 MB |
+| `cv-binary.h5` | Binary classification, computer vision models | < 1 MB |
+| `cv-binary-experiments.csv` | Experiment index for `cv-binary` | < 1 MB |
+| `tabrepo-multiclass.h5` | Multiclass classification, classical tabular models | ~115 MB |
+| `tabrepo-multiclass-experiments.csv` | Experiment index for `tabrepo-multiclass` | < 1 MB |
+| `tabarena-multiclass.h5` | Multiclass classification, modern tabular foundation models | ~11 MB |
+| `tabarena-multiclass-experiments.csv` | Experiment index for `tabarena-multiclass` | < 1 MB |
+| `cv-multiclass.h5` | Multiclass classification, computer vision models | ~39 MB |
+| `cv-multiclass-experiments.csv` | Experiment index for `cv-multiclass` | < 1 MB |
+| `imagenet-multiclass.h5` | 1000-class ImageNet, computer vision models | ~1.5 GB |
+| `imagenet-multiclass-experiments.csv` | Experiment index for `imagenet-multiclass` | < 1 MB |
+
+---
+
+## Benchmark overview
+
+| Benchmark | Problem type | Base models | # Datasets | # Experiments |
+|---|---|---|---|---|
+| `tabrepo-binary` | Binary | 8 | 104 tabular datasets | 832 |
+| `tabarena-binary` | Binary | 11 | 30 tabular datasets | 314 |
+| `cv-binary` | Binary | 9 | 3 (CIFAR-10†, Breast, Pneumonia) | 13 |
+| `tabrepo-multiclass` | Multiclass | 8 | 65 tabular datasets | 520 |
+| `tabarena-multiclass` | Multiclass | 11 | 8 tabular datasets | 84 |
+| `cv-multiclass` | Multiclass | 10 | 6 (CIFAR-10, CIFAR-100, Birds, SVHN, Derma, OCT) | 20 |
+| `imagenet-multiclass` | Large scale multiclass | 8 | 1 (ImageNet) | 8 |
+
+† CIFAR-10 is converted to binary (Animal vs Machine) by marginalising over class groups.
+
+### Base models
+
+**TabRepo** (classical tabular): CatBoost, ExtraTrees, LightGBM, LinearModel, NeuralNetFastAI, NeuralNetTorch, RandomForest, XGBoost. Source: [TabRepo](https://github.com/autogluon/tabrepo) repository `D244_F3_C1530_200`.
+Best hyperparameter configuration selected per (dataset, model, fold) by validation error.
+
+**TabArena** (modern tabular): TabPFN-v2.6, TabICLv2, RealTabPFN-v2.5, TabICL\_GPU, LimiX\_GPU, TabM\_GPU, RealMLP\_GPU, BetaTabPFN\_GPU, ModernNCA\_GPU, Mitra\_GPU, TabDPT\_GPU.
+Models selected with ≥ 1300 ELO on the [TabArena leaderboard](https://huggingface.co/spaces/TabArena/leaderboard) (Classification, All Datasets, as of April 1 2026).
+Source: [TabArena](https://github.com/autogluon/tabarena).
+
+**Computer vision**: ResNet, DenseNet, WideResNet, ViT, BEiT, ConvNeXt, Swin, EVA, and others depending on the dataset.
+Logits sourced from two collections: [NN_calibration](https://github.com/markus93/NN_calibration/tree/master/logits) and [Beyond Overconfidence](https://zenodo.org/records/15229730).
+
+---
+
+## Data format
+
+### HDF5 files
+
+Each `.h5` file has the following structure:
+
+```
+{dataset}/
+  {model}/
+    probas_cal   float32  (n_cal,)           # positive-class probabilities [binary]
+                 float32  (n_cal, n_classes) # class probabilities [multiclass]
+    labels_cal   int32    (n_cal,)
+    probas_test  float32  (n_test,)          # same shape conventions as above
+    labels_test  int32    (n_test,)
+```
+
+File-level attributes:
+- `source` — `"tabrepo"`, `"tabarena"`, `"cv"`, or `"imagenet"`
+- `problem_type` — `"binary"` or `"multiclass"`
+
+All probabilities are valid (non-negative, sum to 1 for multiclass). Labels are 0-indexed integers.
+
+### Experiment CSV files
+
+Each `{benchmark}-experiments.csv` lists one row per (dataset, model) pair:
+
+| Column | Description |
+|---|---|
+| `dataset` | Dataset name (matches the HDF5 group key) |
+| `model` | Model name (matches the HDF5 group key) |
+| `cal_size` | Number of calibration samples |
+| `test_size` | Number of test samples |
+| `n_classes` | Number of classes (multiclass benchmarks only) |
+| `tabrepo_fold` / `tabarena_fold` | Fold index used (TabRepo/TabArena benchmarks) |
+| `tabrepo_config` / `tabarena_config` | Best hyperparameter configuration selected (TabRepo/TabArena) |
+
+---
+
+## Loading the data
+
+### Python (h5py)
+
+```python
+import h5py
+import numpy as np
+
+with h5py.File("tabrepo-binary.h5", "r") as f:
+    # List all (dataset, model) pairs
+    pairs = [(ds, mdl) for ds in f for mdl in f[ds]]
+
+    # Load a single experiment
+    grp = f["anneal/CatBoost"]
+    p_cal   = grp["probas_cal"][:]   # shape (n_cal,)
+    y_cal   = grp["labels_cal"][:]   # shape (n_cal,)
+    p_test  = grp["probas_test"][:]  # shape (n_test,)
+    y_test  = grp["labels_test"][:]  # shape (n_test,)
+```
+
+### With the CalArena runner
+
+The [CalArena repository](https://github.com/super-anonymous-researcher/CalArena) provides `run_benchmark.py`, which loads these files automatically and runs all calibrators:
+
+```bash
+# Place .h5 and .csv files under calibration_benchmarks/
+python run_benchmark.py --benchmark tabrepo-binary
+```
+
+---
+
+## Dataset construction
+
+Scripts that were used to generate the benchmarks files can be found in the [CalArena repository](https://github.com/super-anonymous-researcher/CalArena).
+
+### Calibration / test split
+
+For TabRepo and TabArena, the calibration split corresponds to the **validation fold** of the respective repository, and the test split is the **held-out test set**.
+This ensures no data leakage: the base model never sees the calibration set during training.
+
+For computer vision datasets, the calibration and test splits are fixed partitions provided by the original data sources.
+
+### Excluded datasets
+
+The following datasets were excluded due to errors in the upstream repositories:
+
+- **TabRepo binary**: MiniBooNE
+- **TabRepo multiclass**: jannis, kropt, shuttle
+
+---
+
+## Intended use
+
+This dataset is intended for:
+- Benchmarking post-hoc calibration algorithms on diverse classification tasks
+- Studying the relationship between model type, dataset characteristics, and calibration difficulty
+- Developing new calibration methods with access to pre-computed probability estimates
+
+---
+
+## License
+
+The benchmark data is released under **CC BY 4.0**. Downstream datasets (OpenML, CIFAR, ImageNet, etc.) retain their original licenses; please consult the respective sources before redistribution.
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{calarena2025,
+  title     = {CalArena: A Large-Scale Benchmark for Post-Hoc Calibration},
+  author    = {...},
+  booktitle = {...},
+  year      = {2025},
+}
+```