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skolfus/kviskr-punctuation-no
kviskr-punctuation-no is a token classification model from skolfus. Use it when you need labels on individual words, such as names. It is set up for onnx. The card lists the license as apache-2.0.
Superseded by skolfus/kviskr-tegnsetting-no, which is about 14× smaller, scores higher on Norwegian dictation and spontaneous speech, and is built so it cannot change a word. This model is kept as a research baseline.
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.onnx279 MB · 97%
From the Hugging Face model README
Superseded by
skolfus/kviskr-tegnsetting-no, which is about 14× smaller, scores higher on Norwegian dictation and spontaneous speech, and is built so it cannot change a word. This model is kept as a research baseline.
An INT8-quantized ONNX export of 1-800-BAD-CODE/xlm-roberta_punctuation_fullstop_truecase (XLM-RoBERTa token classifier, 47 languages), evaluated on Norwegian dictation. Boolean outputs are cast to int64 for ONNX Runtime on macOS.
| File | Contents |
|---|---|
model_embed.onnx | INT8-quantized model (~279 MB) |
tokenizer.json | XLM-R tokenizer (swift-transformers compatible) |
tokenizer_config.json, config.json | Tokenizer and model config |
Measured on real verbatim ASR output (NB-Whisper large verbatim) over the National Library's evaluation set (200 clips, many speakers, five domains) and a labelled dictation set:
The model classifies subwords, so it can break abbreviations: bl.a. → b.l...a., m.v. → m...v.. On a held-out text benchmark this affected 9.3% of chunks (279 of 3,000). On ASR text it is rare (1 of 327 clips), since the speech model writes «blant annet», not «bl.a.». The successor model fixes this by predicting per word.
Apache 2.0, the same as the base model. Quantization and evaluation by Kviskr.