Instructions to use prajwalJumde/LayoutLM_test_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajwalJumde/LayoutLM_test_docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="prajwalJumde/LayoutLM_test_docvqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("prajwalJumde/LayoutLM_test_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("prajwalJumde/LayoutLM_test_docvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from prajwalJumde/LayoutLM_test_docvqa: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/prajwalJumde/LayoutLM_test_docvqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://prajwalJumde/LayoutLM_test_docvqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prajwalJumde/LayoutLM_test_docvqa/resolve/main/pytorch_model.bin
802 MB
- Xet hash:
- 9cb13f6be5e6d63f4ac3d5f0e128fb4d7843e413cb9813e92e00704ed99e91b8
- Size of remote file:
- 802 MB
- SHA256:
- e095bcb04d58fb64f939f70ddbbc6796ae8e3dbbd79f822abbf77202669c0753
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