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 pytorch_model.bin from midoiv/result: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/midoiv/result/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://midoiv/result/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/midoiv/result/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 8e25732b6ccdae4d950b0658377a0f52015cc45a1e3b3169f9ce8215d5206033
- Size of remote file:
- 1.26 GB
- SHA256:
- 212ca14a7907f2ae808b4e64926f3307f1ea75e3b7198077acf18cc0c618735d
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