Instructions to use AKASHR/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AKASHR/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AKASHR/layoutlm-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AKASHR/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("AKASHR/layoutlm-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AKASHR/layoutlm-funsd: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/AKASHR/layoutlm-funsd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://AKASHR/layoutlm-funsd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AKASHR/layoutlm-funsd/resolve/main/pytorch_model.bin
451 MB
- Xet hash:
- 4d4ac75d8a0b7a82a7bb79dc94d19139b22d53b03cb13c254ba93cc12095363a
- Size of remote file:
- 451 MB
- SHA256:
- 338346369559734b67f0c66edd7cada768b783192386fca025fe1d312fa82de0
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