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 training_args.bin from httran96/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/httran96/layoutlm-funsd/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://httran96/layoutlm-funsd@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/httran96/layoutlm-funsd/resolve/refs%2Fpr%2F1/training_args.bin
3.58 kB
- Xet hash:
- 4061b40a605fcce0aa2280d8d90199c678a3afc60a5c06a9d1a9988b27da3e5d
- Size of remote file:
- 3.58 kB
- SHA256:
- 4b7b60330ef3ecdef55924f86fe2563a4edc580f746b1b76c22c3bd8d203da36
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.