| --- |
| license: mit |
| language: |
| - en |
| metrics: |
| - accuracy |
| base_model: |
| - google-bert/bert-base-uncased |
| pipeline_tag: zero-shot-classification |
| library_name: adapter-transformers |
| --- |
| |
| # RigelClauseNet: BERT-Based Fraud Clause Detector |
|
|
| **RigelClauseNet** is a fine-tuned BERT-based binary classifier that detects **fraudulent, high-risk, or suspicious clauses** in legal and policy-related documents, including privacy policies, loan agreements, and terms of service. |
|
|
| It is designed to help: |
| - Legal analysts |
| - Fintech systems |
| - Regulatory auditors |
| - End users seeking clarity in digital contracts |
|
|
| --- |
|
|
| ## 🔍 Use Case |
|
|
| Given a clause or paragraph from a document, the model outputs: |
| - A binary risk label (`SAFE`, `RISKY`) |
| - A probability confidence score |
| - A breakdown of class probabilities |
|
|
| This enables organizations to **flag suspicious clauses early**, audit contracts, and build smarter compliance pipelines. |
|
|
| --- |
|
|
| ## 🧠 Model Details |
|
|
| - **Base Model**: `google-bert/bert-base-uncased` |
| - **Architecture**: BERT + Sequence Classification Head |
| - **Training Data**: 5,000 semi-synthetic and curated clauses (labeled as `SAFE` or `RISKY`) |
| - **Classes**: |
| - `0` → Safe clause |
| - `1` → Fraudulent/risky clause |
| - **Trained On**: Google Colab with Hugging Face Transformers |
| - **Performance**: |
| - Accuracy: **98.47%** |
| - Precision: **99.19%** |
| - Recall: **99.19%** |
| - F1 Score: **99.99%** *(on validation set)* |
|
|
| --- |
|
|
| ## 📌 Examples |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch.nn.functional as F |
| import torch |
| |
| model = AutoModelForSequenceClassification.from_pretrained("nitinsri/RigelClauseNet") |
| tokenizer = AutoTokenizer.from_pretrained("nitinsri/RigelClauseNet") |
| |
| def predict_clause(text): |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128) |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| probs = F.softmax(logits, dim=1) |
| label = torch.argmax(probs).item() |
| return { |
| "label": "RISKY" if label == 1 else "SAFE", |
| "confidence": round(probs[0][label].item(), 4), |
| "probabilities": probs.tolist() |
| } |
| |
| # Example |
| predict_clause("Late payments will incur a 25% monthly penalty.") |
| ``` |
|
|
| ## 🧠 Intended Usage |
|
|
| You can use this model for: |
|
|
| - Scanning uploaded PDFs, contracts, or policies |
| - Highlighting or flagging suspicious legal language |
| - Powering backend systems in legal-tech and compliance |
|
|
| --- |
|
|
| ## 🚫 Limitations |
|
|
| - Trained on **semi-synthetic clauses**, not actual legal corporations. |
| - Binary classifier only — it does not explain why a clause is risky. |
| - Contextual or nested document logic is not supported (yet). |
|
|
| --- |
|
|
| ## 📂 Files |
|
|
| | File | Description | |
| |------|-------------| |
| | `model.safetensors` | Fine-tuned model weights | |
| | `config.json` | BERT classification head config | |
| | `tokenizer.json` | Tokenizer for preprocessing | |
| | `vocab.txt` | BERT vocabulary | |
|
|
| --- |
|
|
| ## 💡 Future Plans |
|
|
| - Multi-class classification (`safe`, `risky`, `ambiguous`) |
| - Explanation layer (highlight key tokens that trigger risk) |
| - Full document-level context scanning |
| - Integration with Hugging Face Spaces (with UI) |
|
|
| --- |
|
|
| ## 👨💻 Author |
|
|
| Built by [Nithin Sri] |
| 🚀 Hugging Face: [https://huggingface.co/nitinsri](https://huggingface.co/nitinsri) |
| 📧 Email: nitinjuttuka63@gmaiol.com |
|
|
| --- |
|
|
| ## 📜 License |
|
|
| MIT License |
|
|
| --- |
|
|
| > “Clarity and transparency in digital contracts are not luxuries — they are rights. RigelGuard helps enforce that.” |
|
|
| --- |
