Instructions to use cxlrd/revix-classifier_3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cxlrd/revix-classifier_3.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="cxlrd/revix-classifier_3.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("cxlrd/revix-classifier_3.0") model = AutoModelForAudioClassification.from_pretrained("cxlrd/revix-classifier_3.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cxlrd/revix-classifier_3.0: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/cxlrd/revix-classifier_3.0/resolve/0cfa20f3bf13edbce41c75e792d7e3181288b03d/training_args.bin
- Command line
-
hf download hf://cxlrd/revix-classifier_3.0@0cfa20f3bf13edbce41c75e792d7e3181288b03d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cxlrd/revix-classifier_3.0/resolve/0cfa20f3bf13edbce41c75e792d7e3181288b03d/training_args.bin
5.84 kB
- Xet hash:
- d900d5953740e8a8dcbb2c3bb7113c3f829dfd1e35490776077ffb90b6dc5a55
- Size of remote file:
- 5.84 kB
- SHA256:
- db27c402cfbe1000acd2f43fa516f5b82b18db5c44335279ec42c8f59fd926c4
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