Instructions to use xiamrr/layoutlmv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiamrr/layoutlmv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="xiamrr/layoutlmv3")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("xiamrr/layoutlmv3") model = AutoModelForTokenClassification.from_pretrained("xiamrr/layoutlmv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cffbd271d7aa31ab4bd6e893d91524677c087a6204c3e346ff6bf78970d8227
- Size of remote file:
- 3.31 kB
- SHA256:
- 6138a8d307c460036bc8394e20579c947c9c1356333079336908ac85d8ba6170
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.