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
Download NOTICE from RyeAI/ekko-pnc: direct link, hf CLI and curl.
- Browser
- Download file 445 Bytes
-
https://huggingface.co/RyeAI/ekko-pnc/resolve/main/NOTICE
- Command line
-
hf download hf://RyeAI/ekko-pnc/NOTICE
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curl -L -o NOTICE https://huggingface.co/RyeAI/ekko-pnc/resolve/main/NOTICE
445 Bytes
| Ekko PnC v2 is derived from jonfd/electra-small-nordic (CC BY 4.0). | |
| Modifications by RyeAI: a fused Danish punctuation and capitalization head, | |
| a running-text fine-tune, a boundary-logit calibration folded into | |
| the classifier bias, and ONNX exports. Training text included CoRal, FT Speech | |
| and NST; CoRal and FT Speech punctuation was restored by GPT-4o (Azure OpenAI). | |
| See MODEL_LICENSE.md and LICENSES/ for applicable terms and attribution. | |