metametric

Name	metametric JSON
Version	0.1.2 JSON
	download
home_page	https://omnuy.me/metametric
Summary	A Unified View of Evaluation Metrics for Structured Prediction
upload_time	2024-08-10 02:35:40
maintainer	None
docs_url	None
author	Tongfei Chen
requires_python	>=3.9
license	None
keywords
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # metametric

The `metametric` Python package offers a set of tools for quickly and easily defining and implementing evaluation metrics for a variety of structured prediction tasks in natural language processing (NLP) based on the framework presented in the following paper:

> [A Unified View of Evaluation Metrics for Structured Prediction](https://arxiv.org/abs/2310.13793). Yunmo Chen, William Gantt, Tongfei Chen, Aaron Steven White, and Benjamin Van Durme. *EMNLP 2023*.

The key features of the package include:

- A decorator for automatically defining and implementing a custom metric for an arbitrary `dataclass`.
- A collection of generic components for defining arbitrary new metrics based on the framework in the paper.
- Implementations of a number of metrics for common structured prediction tasks.


To install, run:
```bash
pip install metametric
```

If you use this codebase in your work, please cite the following paper:

```tex
@inproceedings{metametric,
    title={A Unified View of Evaluation Metrics for Structured Prediction},
    author={Yunmo Chen and William Gantt and Tongfei Chen and Aaron Steven White and Benjamin {Van Durme}},
    booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
    year={2023},
    address={Singapore},
}
```

            

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    "description": "# metametric\n\nThe `metametric` Python package offers a set of tools for quickly and easily defining and implementing evaluation metrics for a variety of structured prediction tasks in natural language processing (NLP) based on the framework presented in the following paper:\n\n> [A Unified View of Evaluation Metrics for Structured Prediction](https://arxiv.org/abs/2310.13793). Yunmo Chen, William Gantt, Tongfei Chen, Aaron Steven White, and Benjamin Van Durme. *EMNLP 2023*.\n\nThe key features of the package include:\n\n- A decorator for automatically defining and implementing a custom metric for an arbitrary `dataclass`.\n- A collection of generic components for defining arbitrary new metrics based on the framework in the paper.\n- Implementations of a number of metrics for common structured prediction tasks.\n\n\nTo install, run:\n```bash\npip install metametric\n```\n\nIf you use this codebase in your work, please cite the following paper:\n\n```tex\n@inproceedings{metametric,\n    title={A Unified View of Evaluation Metrics for Structured Prediction},\n    author={Yunmo Chen and William Gantt and Tongfei Chen and Aaron Steven White and Benjamin {Van Durme}},\n    booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},\n    year={2023},\n    address={Singapore},\n}\n```\n",
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