Instructions to use hsali/few_shots_data_1002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsali/few_shots_data_1002 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("hsali/few_shots_data_1002") model = Wav2Vec2ForSpeechClassification.from_pretrained("hsali/few_shots_data_1002", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hsali/few_shots_data_1002: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/hsali/few_shots_data_1002/resolve/main/training_args.bin
- Command line
-
hf download hf://hsali/few_shots_data_1002/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hsali/few_shots_data_1002/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 0d485cd7aea46242bc0361759b18ed86c0f7a9328ce7fcfcfbc986c6262c7650
- Size of remote file:
- 3.52 kB
- SHA256:
- 2d49250139403fe2445aa6a6a77a6a5c8faea3ff26c0f0617add623db79fea7f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.