Instructions to use brandaobrandisborges/layoutlm-synth3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandaobrandisborges/layoutlm-synth3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brandaobrandisborges/layoutlm-synth3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brandaobrandisborges/layoutlm-synth3") model = AutoModelForTokenClassification.from_pretrained("brandaobrandisborges/layoutlm-synth3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from brandaobrandisborges/layoutlm-synth3: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/brandaobrandisborges/layoutlm-synth3/resolve/main/training_args.bin
- Command line
-
hf download hf://brandaobrandisborges/layoutlm-synth3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/brandaobrandisborges/layoutlm-synth3/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 5beca4e686047c139011b9781a0c5b6c6ed3ddf9491572bf1b789d40ec021567
- Size of remote file:
- 3.58 kB
- SHA256:
- fb946e8e5f189b2ee0242202261ab5334d5a7e64c2a3399505b905badc785e9b
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