Instructions to use MBARKI/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBARKI/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MBARKI/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MBARKI/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("MBARKI/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MBARKI/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/MBARKI/layoutlm-funsd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MBARKI/layoutlm-funsd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MBARKI/layoutlm-funsd/resolve/main/pytorch_model.bin
451 MB
- Xet hash:
- 84bc525fa5e4e415d0b88e9bda2cd9aad83478b076cbfd6a114d2c3811ae24d0
- Size of remote file:
- 451 MB
- SHA256:
- f7bf82cbc92810450e198734acb6f712bdced6f4b6a8af657583a97cca28b0cb
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