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
Link public Ekko STT source repository
Browse files
README.md
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@@ -149,6 +149,9 @@ every input word in order. Pass continuous text when possible so the model can
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use context across ASR chunks. These settings affect inference, not the v2
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model weights.
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Version 2 is tuned for **continuous speech** such as dictation and meetings.
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The earlier version often missed sentence endings when given a long transcript;
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it had mainly learned from short, isolated utterances. Version 2 was trained
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use context across ASR chunks. These settings affect inference, not the v2
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model weights.
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The reusable inference runtime and browser demo are in the
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[Ekko STT repository](https://github.com/Rye-A1/ekko-danish-stt).
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Version 2 is tuned for **continuous speech** such as dictation and meetings.
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The earlier version often missed sentence endings when given a long transcript;
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it had mainly learned from short, isolated utterances. Version 2 was trained
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