Image-to-Text
Transformers
PyTorch
vision-encoder-decoder
image-text-to-text
donut
vision
endpoints-template
Instructions to use philschmid/donut-base-finetuned-cord-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/donut-base-finetuned-cord-v2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="philschmid/donut-base-finetuned-cord-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("philschmid/donut-base-finetuned-cord-v2") model = AutoModelForMultimodalLM.from_pretrained("philschmid/donut-base-finetuned-cord-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9960e50963fe06a8be67094d72bcc248c613c5c861c6ecb20658f98b3d972533
- Size of remote file:
- 806 MB
- SHA256:
- 31b78e3d3891072de8e2bf3553b71782242a1f3b589b914ec2b03feff7b14c54
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.