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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tags:
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- token-classification
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- danish
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- punctuation
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- truecasing
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- onnx
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metrics:
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- f1
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---
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[Evaluation harness](https://github.com/Rye-A1/danish-asr-leaderboard)
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# Ekko PnC
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A 21,880,202-parameter Danish punctuation and capitalization model with one
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10-class head: five punctuation labels crossed with lowercase/uppercase.
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tags:
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- token-classification
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- danish
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- danish-nlp
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- punctuation
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- punctuation-restoration
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- truecasing
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- onnx
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- offline
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- local-inference
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metrics:
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- f1
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[Evaluation harness](https://github.com/Rye-A1/danish-asr-leaderboard)
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# Ekko PnC: Danish Punctuation and Capitalization
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A 21,880,202-parameter Danish punctuation and capitalization model with one
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10-class head: five punctuation labels crossed with lowercase/uppercase.
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