Object Detection
Transformers
PyTorch
TensorBoard
layoutlm
token-classification
Generated from Trainer
endpoints-template
Instructions to use Narsil/layoutlm-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/layoutlm-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Narsil/layoutlm-funsd")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/layoutlm-funsd") model = AutoModelForTokenClassification.from_pretrained("Narsil/layoutlm-funsd") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5bb9febde992009d22e5ee68c8d2508d157f7f0b8e6f6c4eb47025a2a0466a8e
- Size of remote file:
- 3.38 kB
- SHA256:
- c56fc4a68a8102016f0d13df85e3cef173b08bfd50400f2f88c520a325d11676
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.