| # FinEE Production Readiness Report | |
| ## Current Status vs Production Target | |
| | Aspect | Current Status | Target | Gap | | |
| |--------|---------------|--------|-----| | |
| | **Training Data** | 137,267 samples | 50,000+ | ✅ **EXCEEDED** | | |
| | **Bank Coverage** | 8 banks | 15+ banks | ⚠️ Need 7 more | | |
| | **Document Types** | Email, SMS | Email, PDF, SMS, Images | ⚠️ PDF/Image parsers added | | |
| | **Evaluation** | F1=56.8% (regex) | F1 > 95% | ❌ LLM fine-tuning needed | | |
| | **Deployment** | Mac (MLX) | Cloud + Mobile + Edge | ⚠️ Export scripts ready | | |
| | **Users** | 0 external | 10+ active | ❌ Beta testing needed | | |
| ## Benchmark Results | |
| ### Regex Extractor (Baseline) | |
| | Field | Accuracy | Status | | |
| |-------|----------|--------| | |
| | Amount | 85.8% | ✅ Good | | |
| | Type | 65.0% | ⚠️ Needs improvement | | |
| | Bank | 100% | ✅ Excellent | | |
| | Merchant | 28.3% | ❌ LLM needed | | |
| | Category | 15.8% | ❌ LLM needed | | |
| | **Overall** | **56.8%** | ❌ Below target | | |
| ### Expected with LLM Fine-tuning | |
| | Field | With LLM | Target | | |
| |-------|----------|--------| | |
| | Amount | ~98% | 99% | | |
| | Type | ~97% | 98% | | |
| | Bank | 100% | 100% | | |
| | Merchant | ~92% | 95% | | |
| | Category | ~88% | 90% | | |
| | **Overall** | **~95%** | 95% | | |
| ## Priority Actions | |
| ### High Priority (This Week) | |
| 1. **Fine-tune LLM on 137K dataset** | |
| - Use `scripts/finetune.py` with MLX or PyTorch | |
| - Target: Phi-3 or Llama 3.1 8B | |
| - Expected improvement: +40% F1 | |
| 2. **Add remaining banks** | |
| - BOB, Canara, Union, IDBI, Federal, South Indian, Karur Vysya | |
| - Update `scripts/data_pipeline/generate_synthetic.py` | |
| 3. **Test PDF parsing** | |
| - Collect sample bank statements | |
| - Test with `src/finee/pdf_parser.py` | |
| ### Medium Priority (This Month) | |
| 4. **Export to ONNX** | |
| - Run `scripts/export_model.py --format onnx` | |
| - Test inference speed | |
| 5. **Deploy to HF Inference** | |
| - Push model to Hugging Face | |
| - Enable Inference API | |
| 6. **Get beta users** | |
| - Share demo: https://huggingface.co/spaces/Ranjit0034/finee-demo | |
| - Collect feedback | |
| ### Low Priority (Next Month) | |
| 7. **Mobile deployment (GGUF)** | |
| 8. **Multi-turn agent** | |
| 9. **Knowledge graph integration** | |
| ## Files Added | |
| | File | Description | | |
| |------|-------------| | |
| | `src/finee/rag.py` | RAG engine with 50+ merchants | | |
| | `src/finee/api.py` | FastAPI backend (8 endpoints) | | |
| | `src/finee/ui.py` | Gradio web interface | | |
| | `src/finee/pdf_parser.py` | PDF/Image parsing | | |
| | `scripts/benchmark.py` | Production benchmark suite | | |
| | `scripts/export_model.py` | ONNX/GGUF/CoreML export | | |
| | `tests/test_rag.py` | 33 comprehensive tests | | |
| ## Commands | |
| ```bash | |
| # Run benchmark | |
| python scripts/benchmark.py --test-file data/instruction/test.jsonl --max-samples 1000 | |
| # Fine-tune LLM | |
| python scripts/finetune.py --backend mlx --model microsoft/phi-3-mini-4k-instruct | |
| # Export to ONNX | |
| python scripts/export_model.py models/finetuned --format onnx | |
| # Start API server | |
| python -m finee.api --port 8000 | |
| # Launch Gradio UI | |
| python -m finee.ui --port 7860 | |
| ``` | |
| ## Links | |
| - **Demo**: https://huggingface.co/spaces/Ranjit0034/finee-demo | |
| - **Dataset**: https://huggingface.co/datasets/Ranjit0034/finee-dataset | |
| - **Model**: https://huggingface.co/Ranjit0034/finee-phi3-4b | |
| - **Code**: https://huggingface.co/Ranjit0034/finance-entity-extractor | |
| --- | |
| *Last updated: 2026-01-14* | |