Instructions to use AntonioTH/Layout-finetuned-fr-model-107instances107-150epochs-5e-05lr-GPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AntonioTH/Layout-finetuned-fr-model-107instances107-150epochs-5e-05lr-GPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="AntonioTH/Layout-finetuned-fr-model-107instances107-150epochs-5e-05lr-GPU")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("AntonioTH/Layout-finetuned-fr-model-107instances107-150epochs-5e-05lr-GPU") model = AutoModelForDocumentQuestionAnswering.from_pretrained("AntonioTH/Layout-finetuned-fr-model-107instances107-150epochs-5e-05lr-GPU") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee80837addefdf9dd9d0ab5b4a1f0421965ac214a012ce568721a7c3ca1cdeeb
- Size of remote file:
- 1.48 GB
- SHA256:
- 80ae9008cff49cd38af513735a874ceee681e3d4b313d120390f1d12ad6568d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.