THU-KEG/OmniEvent
A modular deep learning toolkit for training, evaluating, and benchmarking event extraction models on diverse datasets.
Not currently ranked — collecting fresh signals.
star history
OmniEvent is a unified event extraction framework built on PyTorch and HuggingFace Transformers. It provides standardized data processing pipelines, consistent evaluation metrics, and support for multiple model architectures across various event extraction datasets. The toolkit enables researchers to train custom models and perform comprehensive benchmarks with consistent preprocessing and output formats.
Frequently asked
- What is THU-KEG/OmniEvent?
- A modular deep learning toolkit for training, evaluating, and benchmarking event extraction models on diverse datasets.
- Is OmniEvent open source?
- Yes — THU-KEG/OmniEvent is open source, released under the MIT license.
- What language is OmniEvent written in?
- THU-KEG/OmniEvent is primarily written in Python.
- How popular is OmniEvent?
- THU-KEG/OmniEvent has 407 stars on GitHub.
- Where can I find OmniEvent?
- THU-KEG/OmniEvent is on GitHub at https://github.com/THU-KEG/OmniEvent.