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oliverguhr/fullstop-punctuation-multilingual-base
fullstop-punctuation-multilingual-base is a token classification model from oliverguhr. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
Downloads · 30 days
1.1K
0% of all-time downloads
All-time downloads
1.3M
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Parameters
277M
2.2 GB on disk
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7
Public
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How the weights are stored.
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From the Hugging Face model README
precision recall f1-score support
0 0.99 0.99 0.99 47903344
. 0.94 0.95 0.95 2798780
, 0.85 0.84 0.85 3451618
? 0.88 0.85 0.87 88876
- 0.61 0.32 0.42 157863
: 0.72 0.52 0.60 103789
accuracy 0.98 54504270
macro avg 0.83 0.75 0.78 54504270
weighted avg 0.98 0.98 0.98 54504270
@article{guhr-EtAl:2021:fullstop,
title={FullStop: Multilingual Deep Models for Punctuation Prediction},
author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim},
booktitle = {Proceedings of the Swiss Text Analytics Conference 2021},
month = {June},
year = {2021},
address = {Winterthur, Switzerland},
publisher = {CEUR Workshop Proceedings},
url = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf}
}
@misc{https://doi.org/10.48550/arxiv.2301.03319,
doi = {10.48550/ARXIV.2301.03319},
url = {https://arxiv.org/abs/2301.03319},
author = {Vandeghinste, Vincent and Guhr, Oliver},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},
title = {FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}