Instructions to use aayushiii18/riskloop-representative-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aayushiii18/riskloop-representative-checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aayushiii18/riskloop-representative-checkpoints")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aayushiii18/riskloop-representative-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from aayushiii18/riskloop-representative-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
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https://huggingface.co/aayushiii18/riskloop-representative-checkpoints/resolve/main/README.md
- Command line
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hf download hf://aayushiii18/riskloop-representative-checkpoints/README.md
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curl -L -o README.md https://huggingface.co/aayushiii18/riskloop-representative-checkpoints/resolve/main/README.md
3.52 kB
| 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. | |