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
Simplify PnC quickstart display
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README.md
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## Quickstart
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```bash
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# Hej, mit navn er Emil. Hvordan går det i dag?
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```
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For longer transcripts, the runtime uses 128-token windows with 12 words of overlap
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and keeps every input word in order. Pass continuous text when possible so PnC can
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use context across speech recognition chunks.
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## Quickstart
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Clone the [Ekko STT repository](https://github.com/Rye-A1/ekko-danish-stt),
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then run this from its root with [uv](https://docs.astral.sh/uv/) and Python 3.11+:
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```bash
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uv sync --locked --no-editable --extra pnc
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uv run --no-sync ekko pnc "hej mit navn er emil hvordan går det i dag"
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```
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Expected output: `Hej, mit navn er Emil. Hvordan går det i dag?`
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The first command installs the inference-only runtime. The first PnC run
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downloads the pinned int8 model from Hugging Face; later runs use the cache.
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For longer transcripts, the runtime uses 128-token windows with 12 words of overlap
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and keeps every input word in order. Pass continuous text when possible so PnC can
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use context across speech recognition chunks.
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