Instructions to use BadreddineHug/LayoutLMv3_97_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_97_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_97_2")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_97_2") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_97_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1f05b74d76a1c829959bb30f4ce58ee6f3a259b6d767a9c6c551556cbb925ae
- Size of remote file:
- 1.43 GB
- SHA256:
- 9eba5283ebf5aab481041dce84923dfa073fd130019648bbfa3946d6e750a6bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.