Instructions to use monideep2255/batch_size_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monideep2255/batch_size_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="monideep2255/batch_size_8")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("monideep2255/batch_size_8") model = AutoModelForCTC.from_pretrained("monideep2255/batch_size_8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6d60e8385cf629ae899f4af63e6923eb388a7d28eed0b8f550f703eedb6a6b0
- Size of remote file:
- 3.52 kB
- SHA256:
- de4d9ab6d156a13b541d1faecc98005ba428cba9d07357b9527e0a1959c75731
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.