| --- |
| license: mit |
| library_name: custom |
| pipeline_tag: visual-document-retrieval |
| datasets: |
| - RKB109/multimodal-document-retrieval-20260802-dataset |
| tags: |
| - synthetic-data |
| - transparent-baseline |
| - multimodal-ai |
| - visual-document-retrieval |
| - document-question-answering |
| - image-to-text |
| - feature-extraction |
| metrics: |
| - accuracy |
| --- |
| |
| # Multimodal Document Retrieval Baseline Baseline Model |
|
|
| ## Model Description |
|
|
| This repository contains a small, transparent prototype model for |
| **Business documents contain meaning in text, tables, layout, and imagery that text-only retrieval can miss.** |
|
|
| The model combines per-label token weights with IDF-weighted evidence |
| retrieval. It was generated for reproducible architecture demonstrations and |
| does not call a hosted LLM. |
|
|
| ## Evaluation |
|
|
| - Held-out synthetic examples: 4 |
| - Accuracy: 1 |
| - Intended metrics: retrieval_accuracy, modality_coverage, recall_at_3 |
|
|
| ## Intended Use |
|
|
| - Architecture prototyping |
| - CI and evaluation examples |
| - Local baseline comparisons |
| - Educational experimentation |
|
|
| ## Hugging Face Task Coverage |
|
|
| - `visual-document-retrieval` |
| - `document-question-answering` |
| - `image-to-text` |
| - `feature-extraction` |
|
|
| ## Limitations and Risks |
|
|
| The starter dataset contains synthetic textual modality descriptors, not sensitive scanned documents. |
|
|
| The dataset is synthetic and small. Do not use this model for consequential |
| decisions without representative data, expert review, and production-grade |
| evaluation. |
|
|
| ## Reproducibility |
|
|
| The linked GitHub repository includes `train.py`, the exact dataset split, |
| evaluation code, and the model JSON format. |
|
|
