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purewhite42/dependency_retriever_f
dependency_retriever_f is a sentence similarity model from purewhite42. Use it when you need a score for how close two texts are. It is set up for transformers. The card lists the license as apache-2.0.
<div align="center" <h1 style="font-size: 1.5em;"[ICLR'25 Spotlight] Rethinking and Improving Autoformalization: Towards a Faithful Metric and a Dependency Retrieval-based Approach</h1 <div style="display: flex; justi…
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From the Hugging Face model README
Please refer to the 📺GitHub repo and 📃Paper for more details.
| Bench | Fmt | Method | Recall@5 | Precision@5 |
|---|---|---|---|---|
| ProofNet | F | BM25 | 0.16% | 0.11% |
| F | DR | 35.52% | 22.89% | |
| F+IF | BM25 | 0.00% | 0.00% | |
| F+IF | DR | 32.47% | 20.32% | |
| Con-NF | F | BM25 | 4.41% | 2.37% |
| F | DR | 24.32% | 14.05% | |
| F+IF | BM25 | 9.86% | 6.95% | |
| F+IF | DR | 27.91% | 17.57% |
purewhite42/dependency_retriever_f: Dense dependency retriever whose inputs are formatted using only formal declarations, SFTed from 🤗BAAI/bge-m3purewhite42/dependency_retriever_f_if: Dense dependency retriever whose inputs are formatted using both formal declarations and informal descriptions, SFTed from 🤗BAAI/bge-m3If you find our work useful in your research, please cite
@inproceedings{
liu2025rethinking,
title={Rethinking and improving autoformalization: towards a faithful metric and a Dependency Retrieval-based approach},
author={Qi Liu and Xinhao Zheng and Xudong Lu and Qinxiang Cao and Junchi Yan},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=hUb2At2DsQ}
}
This project is released under the Apache 2.0 license. Please see the LICENSE file for more information.
Feel free to discuss the paper/data/code with us through issues/emails!