Instructions to use capitaletech/Language-levels-LayoutLMv3-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use capitaletech/Language-levels-LayoutLMv3-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="capitaletech/Language-levels-LayoutLMv3-v5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("capitaletech/Language-levels-LayoutLMv3-v5") model = AutoModelForTokenClassification.from_pretrained("capitaletech/Language-levels-LayoutLMv3-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from capitaletech/Language-levels-LayoutLMv3-v5: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/capitaletech/Language-levels-LayoutLMv3-v5/resolve/main/training_args.bin
- Command line
-
hf download hf://capitaletech/Language-levels-LayoutLMv3-v5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/capitaletech/Language-levels-LayoutLMv3-v5/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- fc7f27861063661cc3fa5e589f4da39df673aa33589779e80a879357eb8955cb
- Size of remote file:
- 5.3 kB
- SHA256:
- f39f628a3926d3a86c721238802e6f6993d6953356ffbda16db0e240d0422bc0
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