Instructions to use BadreddineHug/LayoutLMv3_funds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLMv3_funds with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLMv3_funds")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLMv3_funds") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLMv3_funds", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8bcd85f274e7eea13a6ce66e52efec42a5241ca0d9aa3e6f128cf4b44e6b010
- Size of remote file:
- 504 MB
- SHA256:
- 0ba95193c1d2ab4bff43139899b15865f03b366c09bd5450bd62a4f6999c1f80
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