Instructions to use AKASHR/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AKASHR/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AKASHR/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AKASHR/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("AKASHR/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AKASHR/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/AKASHR/layoutlm-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://AKASHR/layoutlm-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AKASHR/layoutlm-funsd/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 7542fb481867850e53d95d01fc1176b85c0ac4ac43eca5fc43c3356b68ad84e1
- Size of remote file:
- 3.58 kB
- SHA256:
- 6bf5cc7d41da11ab1b405b8d0b487262fb6da2b1e6ae0e577ff3ca4048388611
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