Instructions to use dmusingu/lg-digits-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmusingu/lg-digits-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dmusingu/lg-digits-classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dmusingu/lg-digits-classification") model = AutoModelForAudioClassification.from_pretrained("dmusingu/lg-digits-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d715b51deb982ace39c126df093d1d43f5c7c1b9b8f955ba79bbc6136630e6df
- Size of remote file:
- 5.84 kB
- SHA256:
- f48da41a05b88cfd62cc374d55cee58f8890ea7654db66ea47fa00b9f8def07a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.