Instructions to use pasusarla/donut_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pasusarla/donut_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pasusarla/donut_encoder")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("pasusarla/donut_encoder") model = AutoModel.from_pretrained("pasusarla/donut_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a048034f3783e50c5e97d1a4e1834e079e37d11f7789ccdef681a0a06871c613
- Size of remote file:
- 298 MB
- SHA256:
- b5358be04524d0007b881f3de95089def95abbdc4a2b09d0c86cf60a550c1ae8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.