Token Classification
Transformers
Safetensors
layoutlmv3
Generated from Trainer
Eval Results (legacy)
Instructions to use Abinaya/Layoutlmv3-finetuned-DocLayNet-test-10-21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abinaya/Layoutlmv3-finetuned-DocLayNet-test-10-21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Abinaya/Layoutlmv3-finetuned-DocLayNet-test-10-21")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Abinaya/Layoutlmv3-finetuned-DocLayNet-test-10-21") model = AutoModelForTokenClassification.from_pretrained("Abinaya/Layoutlmv3-finetuned-DocLayNet-test-10-21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff126400fa64ce0797662a5988b3d2c4a9674cdf018d8675e22051698b77dcec
- Size of remote file:
- 5.3 kB
- SHA256:
- 1745da2dead474886ff250d9bd9659b13e97c0a81e6ee31ffd0e11e8f15640dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.