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 training_args.bin from justinhcss/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/justinhcss/layoutlm-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://justinhcss/layoutlm-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/justinhcss/layoutlm-funsd/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- ca4c03641efff66021f6edea300f38b1dd33aa8748ccac1eb274033eeb3db9dc
- Size of remote file:
- 4.54 kB
- SHA256:
- 5dc747ec2a09db62d92ff28b81e57b8b8eee4b0a7caa6110900df591ed61ba0d
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