Instructions to use JBJoyce/DENTAL_CLICK_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JBJoyce/DENTAL_CLICK_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="JBJoyce/DENTAL_CLICK_classifier")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("JBJoyce/DENTAL_CLICK_classifier") model = AutoModelForAudioClassification.from_pretrained("JBJoyce/DENTAL_CLICK_classifier") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -16,7 +16,7 @@ Model was trained for 10 epochs on a limited quantity of speech (~1.5 hours) and
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Thus the model should not be assumed to hold generalizability to other speakers or languages without further
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training data or rigorous testing.
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Model was evaluated for accuracy on a hold out test set of 20% of the available data and scored
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## Uses
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Model can be used via transformers library or via Hugging Face Hosted inference API to the right. I would
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Thus the model should not be assumed to hold generalizability to other speakers or languages without further
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training data or rigorous testing.
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Model was evaluated for accuracy on a hold out test set of 20% of the available data and scored 97%.
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## Uses
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Model can be used via transformers library or via Hugging Face Hosted inference API to the right. I would
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