Instructions to use Bilgee/layoutlmv3-financial-document-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bilgee/layoutlmv3-financial-document-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Bilgee/layoutlmv3-financial-document-classification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Bilgee/layoutlmv3-financial-document-classification") model = AutoModelForSequenceClassification.from_pretrained("Bilgee/layoutlmv3-financial-document-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0cd88f1b66073030ff06be4d0ad67ecc332bd21f58377517a0f8ccbb6e938593
- Size of remote file:
- 504 MB
- SHA256:
- 68ea253797388e7811c8eeb214829606ae6885a720e79257aa83fae9d674997d
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