Instructions to use PCS/Extract_Matic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PCS/Extract_Matic 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="PCS/Extract_Matic")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("PCS/Extract_Matic") model = AutoModelForMultimodalLM.from_pretrained("PCS/Extract_Matic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 959 Bytes
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license: mit
language:
- en
pipeline_tag: image-to-text
datasets:
- katanaml-org/invoices-donut-data-v1
---
## Sparrow - Data extraction from documents with ML
This model is finetuned Donut ML base model on invoices data. Model aims to verify how well Donut performs on enterprise docs.
Mean accuracy on test set: 0.96
Inference:

Training loss:

Sparrow on [GitHub](https://github.com/katanaml/sparrow)
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)
Our website [KatanaML](https://www.katanaml.io)
On [Twitter](https://twitter.com/katana_ml) |