Instructions to use ManelR/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManelR/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ManelR/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ManelR/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("ManelR/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ManelR/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/ManelR/layoutlm-funsd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ManelR/layoutlm-funsd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ManelR/layoutlm-funsd/resolve/main/pytorch_model.bin
451 MB
- Xet hash:
- cc414b8a735b17f48ee88ad8bf0325d988357544cdf030b20c970ec141a4f699
- Size of remote file:
- 451 MB
- SHA256:
- 1c93ce16ce992fbc13d1ce3bff7d11488368c7d46ea2aa16feb36ba512024b14
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.