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 pytorch_model.bin from brandaobrandisborges/layoutlm-synthchecking-padding: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/brandaobrandisborges/layoutlm-synthchecking-padding/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://brandaobrandisborges/layoutlm-synthchecking-padding/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/brandaobrandisborges/layoutlm-synthchecking-padding/resolve/main/pytorch_model.bin
1.36 GB
- Xet hash:
- 81949b0d81611f1cf8b28a153a6b6f37eb08a00d962194f6f22f1500af0a5b9e
- Size of remote file:
- 1.36 GB
- SHA256:
- 39f58bc9e91aa8ee1b3245438356159526b077805302d9f4c753966457a033a0
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