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
| 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) |