Upload README.md

README.md

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+---
+license: apache-2.0
+tags:
+- legal
+- bert
+- legal-bert
+- contract-risk
+- cuad
+- pytorch
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+# RiskLoop Representative Model Checkpoints
+
+This repository hosts the **reproduced representative model checkpoints** for [RiskLoop](https://github.com/aayushiii18/RiskLoop), an NLP system for automated contract risk detection based on Legal-BERT fine-tuning on the CUAD (Contract Understanding Atticus Dataset) benchmark.
+
+> [!IMPORTANT]
+> **Provenance & Reproduction Note:**
+> These checkpoints are **reproduced representative checkpoints** created under strictly controlled, frozen training conditions (Condition A) and evaluated in RiskLoop's frozen Phase 6 test evaluation.
+> They are **NOT** the original historical official checkpoints from earlier development phases, as those original historical binaries were lost. These reproduced models represent the exact frozen representative checkpoints used to generate the Phase 6 decision analysis and final test metrics.
+
+---
+
+## 1. Model Architecture & Training Details
+
+- **Backbone Model:** `nlpaueb/legal-bert-base-uncased`
+- **Model Type:** Single-task span classification models (QA-style start/end logit heads over transformer sequence outputs).
+- **Training Strategy:** Condition A (single-task fine-tuning with fixed seeds, 4 epochs, max sequence length 512, document stride 256).
+
+---
+
+## 2. Model Checkpoint Registry & Integrity Hashes
+
+The repository contains three frozen model binary files (`.pt` PyTorch state dictionaries):
+
+| Checkpoint Filename | Target Contract Task | Experimental Condition | Random Seed | File Size (Bytes) | SHA-256 Hash |
+| :--- | :--- | :--- | :--- | :--- | :--- |
+| `run_03_best_model.pt` | **Cap On Liability** | Condition A | `44` | 438,001,498 | `8689ef00a0718713f4ae8bdf8b48d3441b5d9a2ccd13ae322185862551c6f674` |
+| `run_04_best_model.pt` | **Anti-Assignment** | Condition A | `42` | 438,001,498 | `ea6a75254798342e0cc3925dd8adca1ba9b5bb60c6a631ddf14057748c01947c` |
+| `run_07_best_model.pt` | **Termination For Convenience** | Condition A | `42` | 438,001,498 | `a8a28902ef3dbc28e706135e8599f01cfa8c0e9c4c05cc081818e3027deda119` |
+
+---
+
+## 3. Frozen Phase 6 Test Evaluation Metrics
+
+The reproduced representative checkpoints achieved the following metrics on the held-out frozen CUAD test evaluation set (Phase 6):
+
+| Task Name | Representative Checkpoint | Test F1 Score | Test ROC-AUC | Test PR-AUC |
+| :--- | :--- | :--- | :--- | :--- |
+| **Cap On Liability** | `run_03_best_model.pt` | `0.7727273` | `0.9960492` | `0.9147721` |
+| **Anti-Assignment** | `run_04_best_model.pt` | `0.8083624` | `0.9944473` | `0.9146714` |
+| **Termination For Convenience** | `run_07_best_model.pt` | `0.7483871` | `0.9939074` | `0.7926948` |
+
+---
+
+## 4. Inference Score Interpretation
+
+In RiskLoop's inference engine and Streamlit demo interface:
+- Output scores are **uncalibrated raw logit deltas** ($\text{logit}_1 - \text{logit}_0$).
+- High positive scores indicate strong model activation for clause presence.
+- Scores are **not** calibrated probabilities or confidence percentages.
+
+---
+
+## 5. Usage & Legal Disclaimer
+
+- **Intended Use:** Portfolio evaluation, academic research, and interactive open-source demonstration of contract risk detection.
+- **Legal Disclaimer:** This software and model predictions do **NOT** constitute legal advice, formal contract audit, or legal guarantee. Users should consult qualified legal professionals for actual contract review.