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 training_args.bin from MBARKI/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/MBARKI/layoutlm-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://MBARKI/layoutlm-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MBARKI/layoutlm-funsd/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 84ac2e2a1ffa9ee01419c813b434ba2a1a6c0e6684a5537e8db7d23eb9d4ed06
- Size of remote file:
- 3.52 kB
- SHA256:
- 39508cf996e8a0be58ef3b65e56d6885d1bc249f5190994ad0d74c739a0be84a
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