Instructions to use Vijayalaxmi/LayoutLMv2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vijayalaxmi/LayoutLMv2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vijayalaxmi/LayoutLMv2ForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Vijayalaxmi/LayoutLMv2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("Vijayalaxmi/LayoutLMv2ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea055a455151682004a3d22fb645b34a44e32fe63817227bee0e8091e0fea4cc
- Size of remote file:
- 802 MB
- SHA256:
- 36d15c177ba27f6cb6c0ab41828866d05854e96c3cf0d9d19c551a4fb2bafaa1
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