Instructions to use Tornaid/LayoutLMv2_D1_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tornaid/LayoutLMv2_D1_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tornaid/LayoutLMv2_D1_Classifier")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Tornaid/LayoutLMv2_D1_Classifier") model = AutoModelForSequenceClassification.from_pretrained("Tornaid/LayoutLMv2_D1_Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1ed29b530710afc76d399f5834a25d175ae46205a013db0eaf4ff1afae71a59
- Size of remote file:
- 802 MB
- SHA256:
- 9ad0ef66e54a4563a1ce6ae808a2671ed5e1396be68b611b0b7fe498e034a01a
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