Instructions to use katanaml/layoutlmv2-finetuned-cord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katanaml/layoutlmv2-finetuned-cord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="katanaml/layoutlmv2-finetuned-cord")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("katanaml/layoutlmv2-finetuned-cord") model = AutoModelForTokenClassification.from_pretrained("katanaml/layoutlmv2-finetuned-cord", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from katanaml/layoutlmv2-finetuned-cord: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/katanaml/layoutlmv2-finetuned-cord/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://katanaml/layoutlmv2-finetuned-cord/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/katanaml/layoutlmv2-finetuned-cord/resolve/main/pytorch_model.bin
802 MB
- Xet hash:
- bff31aa2d137332cf45eb53589af1c0e82ecda0bc97822b3fa46292c4a93ad66
- Size of remote file:
- 802 MB
- SHA256:
- 97adc0e49958b781b420b0519004c1052b6574f6b5d5b40f499a508e9f5861e9
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