Instructions to use midoiv/result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midoiv/result with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="midoiv/result")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("midoiv/result") model = AutoModelForAudioClassification.from_pretrained("midoiv/result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from midoiv/result: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/midoiv/result/resolve/main/training_args.bin
- Command line
-
hf download hf://midoiv/result/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/midoiv/result/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 387ec06967c42551551d11892ab9f5a68db82006972e65423051c259ffcf5338
- Size of remote file:
- 3.06 kB
- SHA256:
- 9d79b047e1052342be01779e0801163a045b0a104104fbb10e5fbf14cbd60793
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