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
Align card with canonical release metadata
Browse files
README.md
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library_name: transformers
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pipeline_tag: token-classification
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base_model: jonfd/electra-small-nordic
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tags:
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- token-classification
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- danish
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```bash
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git clone https://github.com/Rye-A1/ekko-danish-stt.git
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cd ekko-danish-stt
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uv sync --extra pnc
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uv run ekko tiny audio.wav --pnc --format json
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```
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in overlapping windows, and keeps one output word for every input word so Tiny's
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native timestamps remain attached.
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library_name: transformers
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pipeline_tag: token-classification
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base_model: jonfd/electra-small-nordic
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datasets:
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- alexandrainst/nst-da
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- alexandrainst/ftspeech
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- CoRal-project/coral-v3
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- wikimedia/wikipedia
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tags:
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- token-classification
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- danish
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```bash
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git clone https://github.com/Rye-A1/ekko-danish-stt.git
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cd ekko-danish-stt
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uv sync --locked --extra pnc
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uv run ekko tiny audio.wav --pnc --format json
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```
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Pinned revision: `b1ff0133c7a2bf63ae6288640c267462229a719f`.
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