Instructions to use selinawisco/result_com_sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selinawisco/result_com_sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="selinawisco/result_com_sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("selinawisco/result_com_sample") model = AutoModelForAudioClassification.from_pretrained("selinawisco/result_com_sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from selinawisco/result_com_sample: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/selinawisco/result_com_sample/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://selinawisco/result_com_sample/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/selinawisco/result_com_sample/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- d96db87996ed0e9d30dcde78d4843b0904d456c4422b3bca0f71d56db979cd5a
- Size of remote file:
- 378 MB
- SHA256:
- e06e2e959a2becd7a4c883fcb6c3c66593f0c7837da8131c8239fb03f5051f58
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