This repository contains the pre-trained Set2Regex 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).

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).

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: