Instructions to use vfu/ccc_doc_vqa_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vfu/ccc_doc_vqa_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="vfu/ccc_doc_vqa_test")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("vfu/ccc_doc_vqa_test") model = AutoModelForDocumentQuestionAnswering.from_pretrained("vfu/ccc_doc_vqa_test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75b1cc4346abc76320b594625ae449603205edc913b65597b860de68a73fbe81
- Size of remote file:
- 4.03 kB
- SHA256:
- 162282dcba9503bb108f15fa788782e68b131e4e7a1ad63aadcf75b0ea635e03
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.