Instructions to use brandaobrandisborges/layoutlm-synthchecking-padding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandaobrandisborges/layoutlm-synthchecking-padding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brandaobrandisborges/layoutlm-synthchecking-padding")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brandaobrandisborges/layoutlm-synthchecking-padding") model = AutoModelForTokenClassification.from_pretrained("brandaobrandisborges/layoutlm-synthchecking-padding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from brandaobrandisborges/layoutlm-synthchecking-padding: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/brandaobrandisborges/layoutlm-synthchecking-padding/resolve/main/training_args.bin
- Command line
-
hf download hf://brandaobrandisborges/layoutlm-synthchecking-padding/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/brandaobrandisborges/layoutlm-synthchecking-padding/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 74e5bbf49241ddecd74d578258442135d4a88a4a0a992963e8346bf193d4fc57
- Size of remote file:
- 3.64 kB
- SHA256:
- 3bb5ed3243e3880b405e990300fee27eb31b49908caa661a19a2b009b7cdf785
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