Instructions to use blaackjack/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blaackjack/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="blaackjack/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("blaackjack/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("blaackjack/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from blaackjack/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/blaackjack/layoutlm-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://blaackjack/layoutlm-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/blaackjack/layoutlm-funsd/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- ff9e3e54f9953fc75029389bf06713862d873cc3cf920e53b97398643c945138
- Size of remote file:
- 5.18 kB
- SHA256:
- aae7b1e3eacc1db87dc196474c01d5a648cc0692a886cbe2d9272e794206ccbb
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