| --- |
| library_name: transformers |
| base_model: Qwen/Qwen3-VL-4B-Instruct |
| language: |
| - zh |
| - en |
| tags: |
| - Docision |
| - document-understanding |
| - visual-question-answering |
| - candidate-scoring |
| - qwen3-vl |
| --- |
| |
| <p align="center"> |
| <img src="assets/docision-hero.svg" alt="SoMark | Docision — Multimodal Intelligence for Document Decisions." width="100%"> |
| </p> |
|
|
| <div align="center" style="width: 100%; text-align: center;"> |
| <img src="assets/docision-wordmark.gif" alt="SoMark | Docision — Document + Decision" width="900" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; height: auto;"> |
| </div> |
| <p align="center"><b>Jev-Inspired Multimodal Decisions for Documents.</b></p> |
| <p align="center"> |
| <a href="https://huggingface.co/SoMarkAI/Docision-1.0"><img src="assets/badge-model.svg" alt="Hugging Face: Docision" height="28"></a> |
| <a href="https://huggingface.co/datasets/SoMarkAI/doc-decision-bench"><img src="assets/badge-benchmark.svg" alt="Benchmark: doc-decision-bench" height="28"></a> |
| </p> |
|
|
| ## 👀 Decision intelligence beyond text |
|
|
| **One document can answer multiple decision questions.** Given the same invoice, a finance team asks whether it is eligible for reimbursement, an archive system asks how to classify it, and a reviewer checks whether signatures and seals are present. There is one document, but more than one decision. |
|
|
| Docision brings candidate-answer decisions to multimodal documents: from document classification and visual attribute recognition to understanding content relationships, applying business rules, and even analyzing engineering drawings and scientific charts. |
| Docision unifies document tasks through one simple interaction: Your Document. Your Question. Your Decision. |
|
|
| **Docision focuses on document understanding and decisions.** Identify document categories, check relationships between fields, verify completeness, and make decisions or route documents under the rules you provide. |
|
|
| - **⚡ Keep the context**: multiple questions about the same image benefit from a multiplicative speedup. |
| - **🧩 Bring your choices**: define candidates independently for each question; both their content and count can vary. |
| - **🔎 Inspect each decision**: receive the model's choice and the probability of each candidate. |
|
|
| > If this project helps you, please give it a ⭐ Star in the top right—it is the greatest encouragement for the developers! |
|
|
| <a id="benchmark"></a> |
|
|
| ## 🎯 Document decisions beyond a 27B model—higher speed, higher accuracy. |
|
|
| <p align="center"> |
| <a href="https://huggingface.co/datasets/SoMarkAI/doc-decision-bench"><picture> |
| <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/SoMarkAI/Docision-1.0/resolve/main/assets/benchmark-table.en.dark.svg"> |
| <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/SoMarkAI/Docision-1.0/resolve/main/assets/benchmark-table.en.svg"> |
| <img src="assets/benchmark-table.en.svg" alt="doc-decision-bench: 5,234 document records. Open on Hugging Face." width="100%"> |
| </picture></a> |
| </p> |
| |
| Covers **document understanding and decisions, and cross-industry classification**, with higher speed and quality. |
|
|
| <p align="center"> |
| <img src="assets/docision-benchmark-results.svg" alt="accuracy: Qwen3-VL-4B 86.78%, Docision 96.18%, JEV-27B-VL 95.72%." width="100%"> |
| </p> |
|
|
|
|
| --- |
|
|
| ## 🤗 Find our model here |
|
|
| <p align="center"> |
| <a href="https://huggingface.co/SoMarkAI/Docision-1.0"><picture> |
| <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/SoMarkAI/Docision-1.0/resolve/main/assets/model-table.en.dark.svg"> |
| <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/SoMarkAI/Docision-1.0/resolve/main/assets/model-table.en.svg"> |
| <img src="assets/model-table.en.svg" alt="Docision-1.0: Open the model on Hugging Face." width="100%"> |
| </picture></a> |
| </p> |
| |
| <a id="start"></a> |
|
|
| ## 🚀 Installation and inference |
|
|
| ```bash |
| git clone https://github.com/SoMarkAI/docision.git |
| cd docision |
| python3 -m venv .venv |
| source .venv/bin/activate |
| python -m pip install -e '.' huggingface_hub |
| hf download SoMarkAI/Docision-1.0 --local-dir models/Docision-1.0 |
| |
| CUDA_VISIBLE_DEVICES=0 python demo_docision.py \ |
| --model models/Docision-1.0 \ |
| --input examples/multiple.json \ |
| --output outputs/document-decisions.json |
| ``` |
|
|
| Results are saved to `outputs/document-decisions.json`. Replace the input file to try your own documents. |
|
|
|
|
| --- |
|
|
| ## 📥 Input and output |
|
|
| One JSON file describes a document image and its questions. The `image` path is resolved relative to the JSON file; each question defines its own question text and candidates: |
|
|
| ```json |
| { |
| "image": "page.png", |
| "questions": [ |
| { |
| "id": "q1", |
| "question": "Which category does this document belong to?", |
| "candidates": {"A": "Invoice", "B": "Receipt", "C": "Contract"} |
| } |
| ] |
| } |
| ``` |
|
|
| Results preserve question order, including each candidate's `probability` and the final choice in `decision`. |
|
|
| Candidates contain only `label`, `text` and `probability`; `decision` reports the selected candidate, its index and tie-handling information. |
|
|
|
|
| ## 🔌 MCP integration |
|
|
| Connect Docision to Codex or Claude Code to inspect local documents, ask multiple questions, and export JSON or CSV results. |
|
|
| Install MCP dependencies from the repository: |
|
|
| ```bash |
| python -m pip install '.[mcp]' |
| ``` |
|
|
| Configure your MCP client to launch this stdio bridge: |
|
|
| ```bash |
| docision mcp --api-url http://127.0.0.1:7860 \ |
| --token-file /absolute/path/to/api-token \ |
| --allowed-root /absolute/path/to/documents \ |
| --output-dir /absolute/path/to/exports |
| ``` |
|
|
| The bridge connects to a running Docision model service. Document and export paths belong to the MCP host; the model runs on the service host. See the [MCP setup instructions](https://github.com/SoMarkAI/docision/blob/main/docs/python-package.md#mcp) for prerequisites and remote GPU connections. |
|
|
| <p align="center">This project is licensed under the <a href="https://github.com/SoMarkAI/docision/blob/main/LICENSE">Apache License 2.0</a>.</p> |
|
|
