---
dataset_info:
  features:
  - name: year
    dtype: string
  - name: id
    dtype: string
  - name: problem
    dtype: string
  - name: solution
    dtype: string
  - name: answer_type
    dtype: string
  - name: source
    dtype: string
  - name: type
    dtype: string
  - name: original_problem
    dtype: string
  - name: original_solution
    dtype: string
  - name: variation
    dtype: int64
  splits:
  - name: train
    num_bytes: 420734.1954022989
    num_examples: 150
  - name: validation
    num_bytes: 420734.1954022989
    num_examples: 150
  - name: test
    num_bytes: 622686.6091954022
    num_examples: 222
  download_size: 822829
  dataset_size: 1464155.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# Putnam-AXIOM Splits for ZIP-FIT

This repository contains the train, validation, and test splits of the Putnam-AXIOM dataset specifically for use with the ZIP-FIT methodology research. The dataset is split as follows:

- **train**: 150 examples
- **validation**: 150 examples
- **test**: 222 examples

These splits are derived from the original 522 Putnam problems found in the main Putnam-AXIOM repository.

## Main Repository

The full dataset with original problems and variations is available in the main repository:
[Putnam-AXIOM/putnam-axiom-dataset-ICML-2025-522](https://huggingface.co/datasets/Putnam-AXIOM/putnam-axiom-dataset-ICML-2025-522)

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("zipfit/Putnam-AXIOM-for-zip-fit-splits")

# Access each split
train_data = dataset["train"]
validation_data = dataset["validation"]
test_data = dataset["test"]
```

## Citation

If you use this dataset, please cite it as follows:

```bibtex
@article{putnam_axiom2025,
  title={Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning},
  author={Aryan Gulati and Brando Miranda and Eric Chen and Emily Xia and Kai Fronsdal and Bruno de Moraes Dumont and Sanmi Koyejo},
  journal={39th International Conference on Machine Learning (ICML 2025)},
  year={2025},
  note={Preprint available at: https://openreview.net/pdf?id=YXnwlZe0yf, ICML paper: https://openreview.net/forum?id=kqj2Cn3Sxr}
}

@article{miranda2024zipfit,
  title={ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment},
  year = {2024},
  journal = {arXiv preprint arXiv:2410.18194},
}
```

## ZIP-FIT Project

For more information on the ZIP-FIT framework, including code and documentation, visit the [ZIP-FIT GitHub repository](https://github.com/brando90/evals-for-autoformalization).

## License

This dataset is licensed under the Apache 2.0.