| title: fastChrF | |
| emoji: ⚡ | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.15.1 | |
| app_file: app.py | |
| short_description: Fast sentence-level ChrF for MBR decoding | |
| python_version: "3.12" | |
| # fastChrF Space | |
| Interactive demo for [fastChrF](https://github.com/jvamvas/fastChrF): fast sentence-level ChrF computation for Minimum Bayes Risk decoding. | |
| ## Modes | |
| - **Default (pairwise):** Computes ChrF between each hypothesis and each reference. Output shape: `(num_hypotheses, num_references)`. | |
| - **Aggregate:** Computes a streamlined aggregate ChrF score per hypothesis across all references. Output shape: `(num_hypotheses,)`. | |
| Enter one hypothesis or reference per line. Advanced options mirror the Python API (`char_order`, `beta`, `remove_whitespace`, `eps_smoothing`). | |
| ## Citation | |
| ```bibtex | |
| @misc{vamvas-sennrich-2024-linear, | |
| title={Linear-time Minimum Bayes Risk Decoding with Reference Aggregation}, | |
| author={Jannis Vamvas and Rico Sennrich}, | |
| year={2024}, | |
| eprint={2402.04251}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |