Token Classification
Transformers
ONNX
Safetensors
Danish
electra
punctuation-restoration
sentence-boundary-detection
truecasing
danish
Instructions to use RyeAI/ekko-pnc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RyeAI/ekko-pnc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RyeAI/ekko-pnc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RyeAI/ekko-pnc") model = AutoModelForTokenClassification.from_pretrained("RyeAI/ekko-pnc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sync ekko-pnc model card and cover
Browse files
README.md
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RyeAI's contribution is released under
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[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and is derived from
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`jonfd/electra-small-nordic`.
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The complete distribution is governed by the composite terms in
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[`MODEL_LICENSE.md`](MODEL_LICENSE.md).
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RyeAI's contribution is released under
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[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and is derived from
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`jonfd/electra-small-nordic`. The retained PnC lineage used Danish Wikipedia for
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text pretraining, followed by NST-da, CoRal, and FT Speech transcript text. Nota
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was not part of this model's retained training lineage. Applicable source-corpus
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notices must accompany the release.
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The complete distribution is governed by the composite terms in
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[`MODEL_LICENSE.md`](MODEL_LICENSE.md).
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