Instructions to use Jacquescv/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jacquescv/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Jacquescv/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Jacquescv/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("Jacquescv/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jacquescv/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/Jacquescv/layoutlm-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://Jacquescv/layoutlm-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jacquescv/layoutlm-funsd/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- 519be5af85a3ec4f1026c3bbcef79244968765d2801ea47e53cf590a8c41c47b
- Size of remote file:
- 3.5 kB
- SHA256:
- ecf46bb511baec9b538a2d5836042b5ab029423d44e9b738615c88a36123d0aa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.