This repository contains the pre-trained Partitioner model presented in the paper ReSyn: A Generalized Recursive Regular Expression Synthesis Framework.

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).

Partitioner performs the Union decomposition. Given a set of positive example strings, it predicts a group label for each string, clustering structurally similar strings together so that each cluster can be synthesized independently and combined with the union operator.

These are custom PyTorch models that use PyTorchModelHubMixin. The model class is defined in the GitHub repository; clone it first so that the ReSyn package is importable, then:

See ReSyn/server.py for the full input encoding / output decoding used at inference time.

If you find this work useful, please cite: