Instructions to use xhyi/layoutlmv3_docvqa_t11c5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xhyi/layoutlmv3_docvqa_t11c5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="xhyi/layoutlmv3_docvqa_t11c5000")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("xhyi/layoutlmv3_docvqa_t11c5000") model = AutoModelForDocumentQuestionAnswering.from_pretrained("xhyi/layoutlmv3_docvqa_t11c5000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 398b811c97e1b72b8461ee3d914905128dc6c9d6f7dd5c0fa87fdd9a4f8c4d6a
- Size of remote file:
- 504 MB
- SHA256:
- 4d675b0188e1f0fa7adcd3a82e551d72ca939efcd86d15389bce6f7aad7c0622
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