Instructions to use Scezui/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scezui/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Scezui/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Scezui/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("Scezui/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Scezui/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/Scezui/layoutlm-funsd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Scezui/layoutlm-funsd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Scezui/layoutlm-funsd/resolve/main/pytorch_model.bin
451 MB
- Xet hash:
- 0a3fae8fb000b07664bc20091a014420bdc4c5ede14c2650daa6ba96e6dacd1d
- Size of remote file:
- 451 MB
- SHA256:
- b541558b9d290976497b3adb03e99ec89275e2014c72dc647c76c570081ec8d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.