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
Use measured 1000-word speed benchmark
Browse files
README.md
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## Speed
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**
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loading. The int8 ONNX benchmark includes tokenization and rendering;
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excludes download, speech recognition, and browser WASM. Speed varies by device
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and input length. [Reproduce the benchmark](https://github.com/Rye-A1/ekko-danish-stt/blob/main/benchmarks/benchmark_pnc_latency.py)
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## Files and use
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## Speed
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**1,000 Danish words in about 0.09 seconds** on an Apple M1 Pro CPU, after model
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loading. The int8 ONNX benchmark includes tokenization, inference, and rendering;
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it excludes download, speech recognition, and browser WASM. Speed varies by device
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and input length. [Reproduce the benchmark](https://github.com/Rye-A1/ekko-danish-stt/blob/main/benchmarks/benchmark_pnc_latency.py)
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with `--words 1000`.
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## Files and use
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