Instructions to use Prem11100/layoutlmv3-Sample2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prem11100/layoutlmv3-Sample2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Prem11100/layoutlmv3-Sample2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Prem11100/layoutlmv3-Sample2") model = AutoModelForSequenceClassification.from_pretrained("Prem11100/layoutlmv3-Sample2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Prem11100/layoutlmv3-Sample2: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/Prem11100/layoutlmv3-Sample2/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Prem11100/layoutlmv3-Sample2@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Prem11100/layoutlmv3-Sample2/resolve/refs%2Fpr%2F1/pytorch_model.bin
504 MB
- Xet hash:
- e05da9cb76242a348b8d2cb5a500645bd5c057dac3c12740db5ccd0eb41f9dd4
- Size of remote file:
- 504 MB
- SHA256:
- fab05ec6807e0ced272f33c9af86626bef997c43e245f6499a7fb7011e3a181e
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