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 training_args.bin from selinawisco/result_com_sample: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/selinawisco/result_com_sample/resolve/main/training_args.bin
- Command line
-
hf download hf://selinawisco/result_com_sample/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/selinawisco/result_com_sample/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 16119e659f6bacb91b2aeca0fd4d53800bb817851839e9e24a15885c218b5993
- Size of remote file:
- 4.03 kB
- SHA256:
- fa6276956e9529eede1f3b2a96f67a8bb1cb0dea348a060f1dd0827af71d4ecd
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