Instructions to use quangtran199hust/layoutlmv2_e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quangtran199hust/layoutlmv2_e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="quangtran199hust/layoutlmv2_e")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("quangtran199hust/layoutlmv2_e") model = AutoModelForTokenClassification.from_pretrained("quangtran199hust/layoutlmv2_e", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from quangtran199hust/layoutlmv2_e: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/quangtran199hust/layoutlmv2_e/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://quangtran199hust/layoutlmv2_e/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/quangtran199hust/layoutlmv2_e/resolve/main/pytorch_model.bin
802 MB
- Xet hash:
- 63581ee41e596d0cbcc32e10a1288776c0ae81ac032e98e8781e8b02d7481ae4
- Size of remote file:
- 802 MB
- SHA256:
- a7101101b7decb2c5c3ffcef8499566d563e9e1fa91d75b0c604eccc6c2c072a
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