Instructions to use FadQ/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FadQ/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="FadQ/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("FadQ/results") model = AutoModelForAudioClassification.from_pretrained("FadQ/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from FadQ/results: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/FadQ/results/resolve/main/training_args.bin
- Command line
-
hf download hf://FadQ/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FadQ/results/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- ac2b71f7005180e0f212bfe9e807418ea7dfec891aafffcc4f512d16f918443c
- Size of remote file:
- 5.3 kB
- SHA256:
- af5e257efd617a24dbd5e8a90678a44b6bfbccd9a90665ac4737befdf594eed4
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