Instructions to use sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sarveediwan/layoutlmv2-base-uncased_finetuned_docvqa/resolve/main/pytorch_model.bin
802 MB
- Xet hash:
- 1227b66725bcdc484dc4d8d09743a7294e8c3566d53b7391be9a92226ead453f
- Size of remote file:
- 802 MB
- SHA256:
- 2db174f7623d5b1074a170d0dc183c681dc732473372553ce6799920decc8f68
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