Instructions to use Sennodipoi/LayoutLMv2-kleisterNDA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sennodipoi/LayoutLMv2-kleisterNDA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sennodipoi/LayoutLMv2-kleisterNDA")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Sennodipoi/LayoutLMv2-kleisterNDA") model = AutoModelForTokenClassification.from_pretrained("Sennodipoi/LayoutLMv2-kleisterNDA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sennodipoi/LayoutLMv2-kleisterNDA: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/Sennodipoi/LayoutLMv2-kleisterNDA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sennodipoi/LayoutLMv2-kleisterNDA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sennodipoi/LayoutLMv2-kleisterNDA/resolve/main/pytorch_model.bin
802 MB
- Xet hash:
- 487d13436e066c6c431db27494d360b91911dc0b0b6cfb800a4e7a0c4c65e571
- Size of remote file:
- 802 MB
- SHA256:
- 3eaa3971c4a7f899f1e3cdbb0583ca962e205d6e0ad3d329e7abacc2a1220258
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