Instructions to use Bgeorge/model_dialect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bgeorge/model_dialect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Bgeorge/model_dialect")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Bgeorge/model_dialect") model = AutoModelForAudioClassification.from_pretrained("Bgeorge/model_dialect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Bgeorge/model_dialect: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Bgeorge/model_dialect/resolve/a80fc05ebf44c55c1e84ec8ca9c725be3087d013/training_args.bin
- Command line
-
hf download hf://Bgeorge/model_dialect@a80fc05ebf44c55c1e84ec8ca9c725be3087d013/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Bgeorge/model_dialect/resolve/a80fc05ebf44c55c1e84ec8ca9c725be3087d013/training_args.bin
5.3 kB
- Xet hash:
- 8768c648f8bb1238308b90d9afc1775828f2d73d1e6f4a66fdd0299f90567218
- Size of remote file:
- 5.3 kB
- SHA256:
- a2279482914cbc565cce3ffedb8e5f42850e03b272eaccaaf16c76ded08dd6c0
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