🏗️ This is a FYP project topic on document parsing of 🚚 logistics 🚚 shipping documents for system integration.
Latest update on the version of modules used to continue run the program because there is no recent update for the donut pretrained model.
My use case: Extract common key datafields from shipping documents generated from ten different shipping lines.
Repo & Datasets
- donut.zip (Original Donut Repo + Labelled Booking Dummy Datasets with JSONL files + Config Files)
- sample-image-to-play.zip (Excess dummy datasets used to play and test the model) https://huggingface.co/spaces/uartimcs/donut-booking-gradio
Colab Notebooks
- donut-booking-train.ipynb (Train the model in Colab using T4 TPU / A100 GPU environment)
- donut-booking-run.ipynb (Run the model in Colab using gradio using T4 TPU / A100 GPU environment)
Size of dataset
Follow the CORD-v2 dataset ratio:
- train: 800 (80 pics x 10 classes)
- validation: 100 (10 pics x 10 classes)
- test: 100 (10 pics x 10 classes)
- Downloads last month
- 64
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
the HF Inference API does not support transformers models with pipeline type image-text-to-text
Model tree for uartimcs/donut-booking-extract
Base model
naver-clova-ix/donut-base-finetuned-cord-v2