Instructions to use brandaobrandisborges/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandaobrandisborges/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brandaobrandisborges/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brandaobrandisborges/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("brandaobrandisborges/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from brandaobrandisborges/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/brandaobrandisborges/layoutlm-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://brandaobrandisborges/layoutlm-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/brandaobrandisborges/layoutlm-funsd/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 521d8800fd3a6eee14f97a8794501cf3c46db71d046659a2c485357e14971589
- Size of remote file:
- 3.52 kB
- SHA256:
- 5950cf34d6366b89e98f9b5703ee1de8397a9180be5dd8f8b888280adeac80af
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