Instructions to use Dino21/financial-risk-extractor-phi3-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dino21/financial-risk-extractor-phi3-qlora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Financial Risk Clause Extractor (Phi-3-mini, QLoRA)
Extracts structured risk factors from corporate disclosure prose.
Input: a passage of filing-style text. Output: JSON listing each distinct risk with a category from a closed twelve-item taxonomy, its trigger, its potential impact, and a severity.
Taxonomy
market_risk, credit_risk, liquidity_risk, operational_risk,
regulatory_risk, cybersecurity_risk, supply_chain_risk,
concentration_risk, geopolitical_risk, technology_risk,
litigation_risk, environmental_risk
Training
| Base | microsoft/Phi-3-mini-4k-instruct |
| Method | QLoRA, 4-bit NF4 with double quantisation |
| LoRA | r=16, alpha=32, dropout 0.05 |
| Target modules | qkv_proj, o_proj, gate_up_proj, down_proj |
| Data | 120 synthetic examples, 96 train / 12 val / 12 test |
| Epochs | 2, selected on validation loss |
| Hardware | single Colab T4, 9 minutes |
Validation loss decreased from 0.6284 to 0.6098 across two epochs. A 3-epoch run was tried first and showed validation loss rising at epoch 3 while training loss kept falling, so 2 was chosen on that evidence.
Training data was generated synthetically by a teacher model. Passages are invented, not real filings, and no real company is named.
Intended use
Built for a technical assessment. Not investment advice, and not validated for production use on real regulatory filings.
Repository
Full pipeline, dataset generation, diversity analysis and evaluation: https://github.com/Dinojan9901/CDAZZDEV-MLE-DINOJAN
- Downloads last month
- -
Model tree for Dino21/financial-risk-extractor-phi3-qlora
Base model
microsoft/Phi-3-mini-4k-instruct