Instructions to use brandaobrandisborges/layoutlm-synth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandaobrandisborges/layoutlm-synth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brandaobrandisborges/layoutlm-synth")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brandaobrandisborges/layoutlm-synth") model = AutoModelForTokenClassification.from_pretrained("brandaobrandisborges/layoutlm-synth", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from brandaobrandisborges/layoutlm-synth: direct link, hf CLI and curl.
- Browser
- Download file 451 MB
-
https://huggingface.co/brandaobrandisborges/layoutlm-synth/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://brandaobrandisborges/layoutlm-synth/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/brandaobrandisborges/layoutlm-synth/resolve/main/pytorch_model.bin
451 MB
- Xet hash:
- c075854be6ce2fef0b04fcfd000d7eaf713963860dbfc3bd348ea61d149499ca
- Size of remote file:
- 451 MB
- SHA256:
- 30fbc39dd15683b6c84b5217b1f4584c41867d07e72fffa7a900ca3f57c03d40
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