Instructions to use BadreddineHug/LayoutLM_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_6")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_6") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08b3f7b1b5247c7db14d522d587900017c15f25aa3711403bfb6e41d288009cd
- Size of remote file:
- 3.96 kB
- SHA256:
- 4839e5c0a0169eea9fe8649a73ae909328d1a476d27d693bbcef113d98e3d4f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.