Instructions to use justinhcss/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use justinhcss/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="justinhcss/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("justinhcss/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("justinhcss/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from justinhcss/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/justinhcss/layoutlm-funsd/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://justinhcss/layoutlm-funsd@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/justinhcss/layoutlm-funsd/resolve/refs%2Fpr%2F1/pytorch_model.bin
451 MB
- Xet hash:
- c4a521babd1afd541d8b9d246b618246a001f0a0c28ea4529845f50e21827577
- Size of remote file:
- 451 MB
- SHA256:
- 924763353868031867002b699f5680a3a4b62705947abc971f373ec89e09ffd6
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