Instructions to use nielsr/layoutlmv2-finetuned-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/layoutlmv2-finetuned-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nielsr/layoutlmv2-finetuned-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("nielsr/layoutlmv2-finetuned-funsd") model = AutoModelForTokenClassification.from_pretrained("nielsr/layoutlmv2-finetuned-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nielsr/layoutlmv2-finetuned-funsd: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/nielsr/layoutlmv2-finetuned-funsd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nielsr/layoutlmv2-finetuned-funsd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nielsr/layoutlmv2-finetuned-funsd/resolve/main/pytorch_model.bin
802 MB
- Xet hash:
- aab023da201f7a0266abac36947781d445e5107ac6043fcc01100d31c02e2abc
- Size of remote file:
- 802 MB
- SHA256:
- 4f1460b3652dbd260b7c7194239762cf7e4944ebf15191dbf4d32598fb1403dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.