| --- |
| license: apache-2.0 |
| library_name: resyn |
| pipeline_tag: text-generation |
| tags: |
| - regex |
| - regex-synthesis |
| - program-synthesis |
| - pytorch |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| - set2regex |
| datasets: |
| - mrseongminkim/ReSyn |
| --- |
| |
| # ReSyn — Set2Regex |
|
|
| This repository contains the pre-trained **Set2Regex** model presented in the paper [ReSyn: A Generalized Recursive Regular Expression Synthesis Framework](https://huggingface.co/papers/2603.24624). |
|
|
| ReSyn is a synthesizer-agnostic divide-and-conquer framework that decomposes complex regular expression synthesis problems into manageable sub-problems by adaptively predicting whether to split examples sequentially (Concatenation) or group them by structural similarity (Union). |
|
|
| **Set2Regex** is the core neural synthesizer. Given a set of positive and negative example strings, it autoregressively generates a regular expression that matches every positive string and rejects every negative string. It encodes the example set with a hierarchical (character-level then string-level) Transformer and decodes the regex with set- and string-conditioned Transformer decoders. Greedy, top-k/top-p sampling, and beam search decoding are supported (see `predict`). |
|
|
| ## Links |
|
|
| - **Paper:** [ReSyn: A Generalized Recursive Regular Expression Synthesis Framework](https://huggingface.co/papers/2603.24624) |
| - **GitHub Repository:** [mrseongminkim/ReSyn](https://github.com/mrseongminkim/ReSyn) |
| - **Dataset:** [mrseongminkim/ReSyn](https://huggingface.co/datasets/mrseongminkim/ReSyn) |
|
|
| ## Usage |
|
|
| These are custom PyTorch models that use [`PyTorchModelHubMixin`](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin). The model class is defined in the [GitHub repository](https://github.com/mrseongminkim/ReSyn); clone it first so that the `ReSyn` package is importable, then: |
|
|
| ```python |
| from ReSyn.model import Set2Regex |
| |
| model = Set2Regex.from_pretrained("mrseongminkim/ReSyn-Set2Regex").eval() |
| ``` |
|
|
| See [`ReSyn/server.py`](https://github.com/mrseongminkim/ReSyn/blob/main/ReSyn/server.py) for the full input encoding / output decoding used at inference time. |
|
|
| ## Citation |
|
|
| If you find this work useful, please cite: |
|
|
| ```bibtex |
| @inproceedings{kim2026resyn, |
| title={ReSyn: A Generalized Recursive Regular Expression Synthesis Framework}, |
| author={Kim, Seongmin and Cheon, Hyunjoon and Kim, Su-Hyeon and Han, Yo-Sub and Ko, Sang-Ki}, |
| booktitle={Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-26)}, |
| year={2026} |
| } |
| ``` |
|
|
