Instructions to use chingoduc/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chingoduc/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chingoduc/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chingoduc/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("chingoduc/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from chingoduc/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/chingoduc/layoutlm-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://chingoduc/layoutlm-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/chingoduc/layoutlm-funsd/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 09acfda34cf831592444a050202a85ff134e3e40d46d71f63866615422c813b5
- Size of remote file:
- 3.52 kB
- SHA256:
- f340d1e7e390a19954d19c8fa66aed617631cbd6026c24e6ba83e48500be3b99
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