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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36717065fe25e0fe8dd61c28b7816da4e3aa379ab1b8419703d71b3521da9ef2
- Size of remote file:
- 802 MB
- SHA256:
- 4da0ba14e1731fc1b5ea28966f3eceab92ae6b6fb5b7726fdf760ef6678764e4
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