Token Classification
Transformers
PyTorch
TensorBoard
layoutlmv3
Generated from Trainer
Eval Results (legacy)
Instructions to use Sebabrata/layoutlmv3-finetuned-cord_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebabrata/layoutlmv3-finetuned-cord_100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sebabrata/layoutlmv3-finetuned-cord_100")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Sebabrata/layoutlmv3-finetuned-cord_100") model = AutoModelForTokenClassification.from_pretrained("Sebabrata/layoutlmv3-finetuned-cord_100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f3da93c851f47bdaa5af3b25c1b2c3980cb9895cb68a3c94802b8b1ff08586c
- Size of remote file:
- 504 MB
- SHA256:
- e5e4f764eeadfc5b4be4b33f62e6e110e29e119d321d5bf531fdf32b8c7a7f8b
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