Instructions to use Prem11100/layoutlmv3-Sample3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prem11100/layoutlmv3-Sample3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Prem11100/layoutlmv3-Sample3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Prem11100/layoutlmv3-Sample3") model = AutoModelForSequenceClassification.from_pretrained("Prem11100/layoutlmv3-Sample3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Prem11100/layoutlmv3-Sample3: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/Prem11100/layoutlmv3-Sample3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Prem11100/layoutlmv3-Sample3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Prem11100/layoutlmv3-Sample3/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 07996079644f38a7d9525ef55d89abfe77468a9caf2e3d4251d1243b53737938
- Size of remote file:
- 504 MB
- SHA256:
- 0056f1e01d333cd7b6587df903b1a62b103582e7c70cbff5512c886f9cefcc37
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