license: apache-2.0
task_categories:
- text-ranking
tags:
- math
- formalization
- lean
library_name: datasets
Premise Retrieval Dataset for Mathematical Formalization
This dataset is used in the paper Learning an Effective Premise Retrieval Model for Efficient Mathematical Formalization. It contains data for training and evaluating a lightweight and effective premise retrieval model for the Lean theorem prover.
About the Dataset
The dataset consists of proof states (acting as queries) and corresponding premises extracted from the Mathlib library. It is designed to facilitate the training of models using a contrastive learning framework to embed queries and premises in a latent space. This process aims to enhance retrieval performance through fine-grained similarity calculation and a re-ranking module, ultimately assisting users in the mathematical formalization process.
Links
- Paper: Learning an Effective Premise Retrieval Model for Efficient Mathematical Formalization
- Project Page: https://premise-search.com/
- Code: The source code and trained models can be found on the GitHub repository.
Dataset Access
This dataset is available for download at this link on the Hugging Face Hub.
Sample Usage
You can load the dataset using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("ruc-ai4math/mathlib_handler_benchmark_410")
# To inspect the 'train' split
print(dataset["train"][0])