Instructions to use curiousily/layoutlmv3-financial-document-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use curiousily/layoutlmv3-financial-document-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="curiousily/layoutlmv3-financial-document-classification", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("curiousily/layoutlmv3-financial-document-classification") model = AutoModelForSequenceClassification.from_pretrained("curiousily/layoutlmv3-financial-document-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56a84136691d4fb49830f8b58b63026fbc6e600786ffdf45f53f86d76cb54dfa
- Size of remote file:
- 504 MB
- SHA256:
- 7659c1286f1e2c50250b78a31bf048c9235247ced870e8781567d4b132f25bd6
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