Instructions to use katanaml-org/invoices-donut-model-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katanaml-org/invoices-donut-model-v1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="katanaml-org/invoices-donut-model-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("katanaml-org/invoices-donut-model-v1") model = AutoModelForMultimodalLM.from_pretrained("katanaml-org/invoices-donut-model-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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This model is finetuned Donut ML base model on invoices data. Model aims to verify how well Donut performs on enterprise docs.
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Inference:
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Sparrow on [GitHub](https://github.com/katanaml/sparrow)
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Our website [KatanaML](https://www.katanaml.io)
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On [Twitter](https://twitter.com/katana_ml)
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This model is finetuned Donut ML base model on invoices data. Model aims to verify how well Donut performs on enterprise docs.
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Mean accuracy on test set: 0.96
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Inference:
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Sparrow on [GitHub](https://github.com/katanaml/sparrow)
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Sample invoice [docs](https://github.com/katanaml/sparrow/tree/main/sparrow-ui/docs/images) to use for inference (docs up to 500 were used for fine-tuning, use docs from 500 for inference)
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Our website [KatanaML](https://www.katanaml.io)
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On [Twitter](https://twitter.com/katana_ml)
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