Instructions to use BadreddineHug/LayoutLMv3_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_1")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_1") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9173a0d3a2d04e48261246dc27b6148014201a7e3865dddde471830abaf8cd51
- Size of remote file:
- 3.96 kB
- SHA256:
- 0f88d78360e9b87fad2556369b0d3f52d7da22324dafe3c3d98b87d5d2fdb60d
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