Instructions to use BadreddineHug/LayoutLMv3_97 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_97 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_97")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_97") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_97", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3764311afc4468a1c7c322af05611faba019e0cf7d1da7003d26a763f544bd1b
- Size of remote file:
- 3.96 kB
- SHA256:
- 6bbfc3c8998f2e6abd49e85e2b6e83d660c1503ff71c1fcf82d156654ba75e49
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.