Instructions to use httran96/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use httran96/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="httran96/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("httran96/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("httran96/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from httran96/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/httran96/layoutlm-funsd/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://httran96/layoutlm-funsd@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/httran96/layoutlm-funsd/resolve/refs%2Fpr%2F1/pytorch_model.bin
451 MB
- Xet hash:
- d6b20ffc54407de1df998ce04c52cadbba220c26af1624b1bb77f46340134e18
- Size of remote file:
- 451 MB
- SHA256:
- 3e758e41c281f0c786eecb10e8854e6ccdcef0fac450704993e83fccc620c397
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.