Instructions to use BadreddineHug/LayoutLMv3_batch_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_batch_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_batch_8")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_batch_8") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_batch_8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6af894319d5272c06da9ee4ac8c1925d350e19926d843dc4f33f69a59ea8e69c
- Size of remote file:
- 1.43 GB
- SHA256:
- 6eb7317dc82d597d128447e1cc587fe086d111f75827d4619cce1751aa7dd1df
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