Instructions to use ritutweets46/layoutlm-funsd-again with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ritutweets46/layoutlm-funsd-again with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ritutweets46/layoutlm-funsd-again")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ritutweets46/layoutlm-funsd-again") model = AutoModelForTokenClassification.from_pretrained("ritutweets46/layoutlm-funsd-again", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ritutweets46/layoutlm-funsd-again: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/ritutweets46/layoutlm-funsd-again/resolve/main/training_args.bin
- Command line
-
hf download hf://ritutweets46/layoutlm-funsd-again/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ritutweets46/layoutlm-funsd-again/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- b884ce95fa91855f6cf16119c7bfd106f501582c2d0dac08e1d0a1d535e00187
- Size of remote file:
- 5.11 kB
- SHA256:
- 5a6aaa4de1fc09e4f9a5e54f02ccd7e3df381057e6b7751e35ed3ec98cb85d14
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.