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. 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:
pip install metametric
If you use this codebase in your work, please cite the following paper:
@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},
}
Metadata
Release files for metametric 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| metametric-0.2.1.tar.gz | 18.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| metametric-0.2.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 44.0 kB
Release files / metametric-0.2.1.tar.gz
| Download URL | metametric-0.2.1.tar.gz |
|---|---|
| Size | 18.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
76df201ae0e215c5ffbfe4f37bab5bfd9ac0e2b900e6bce4cd4183372c4a17fc
|
|
BLAKE2b-256 checksum How to use checksums |
ed7d54af7e1c4fca7bbfe50ea07c78d0c1f8e1d14dbd5c004f8ce0b17ad8dfda
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
uv/0.4.27
|
Release files / metametric-0.2.1-py3-none-any.whl
| Download URL | metametric-0.2.1-py3-none-any.whl |
|---|---|
| Size | 25.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
26c41dd3b3d6bf17000c60b40e7e3eb340092f34e5327a913c493e6ab69611da
|
|
BLAKE2b-256 checksum How to use checksums |
c5bdcb5ac3313aaa82cb6bcc6cdc0416df744559c45b3bc1639a140d395ee9b9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
uv/0.4.27
|