emekaphilians
/

trustchainai-codebert

@@ -1,199 +1,164 @@
 ---
-library_name: transformers
-tags: []
 ---
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 ---
+language: en
+license: mit
+tags:
+  - code
+  - solidity
+  - smart-contracts
+  - security
+  - vulnerability-detection
+  - blockchain
+  - ethereum
+  - defi
+datasets:
+  - smartbugs-curated
+  - solidifi-benchmark
+  - defihacklabs
+  - not-so-smart-contracts
+metrics:
+  - f1
+base_model: microsoft/codebert-base
 ---
+# trustchainai-codebert
+**Fine-tuned CodeBERT for Solidity smart contract vulnerability detection.**
+Part of the [TrustChainAI](https://github.com/emekaphilian/TrustChainAI) project — an AI-powered smart contract auditor with explainability and ethics monitoring, built to make blockchain security accessible to African and emerging-market Web3 ecosystems.
+---
+## Model Performance
+| Metric | Score |
+|---|---|
+| **F1 (weighted, test set)** | **98.6%** |
+| Eval Loss | 0.0428 |
+| Test Samples | 1,032 contracts |
+| Classes | 13 vulnerability categories |
+---
+## How to Use
+```python
+from transformers import pipeline
+classifier = pipeline(
+    "text-classification",
+    model="emekaphilians/trustchainai-codebert"
+)
+contract = """
+pragma solidity ^0.8.0;
+contract Vulnerable {
+    mapping(address => uint) public balances;
+    function withdraw() external {
+        uint amt = balances[msg.sender];
+        (bool ok,) = msg.sender.call{value: amt}("");
+        balances[msg.sender] = 0;
+    }
+}
+"""
+result = classifier(contract[:512])
+print(result)
+# [{'label': 'reentrancy', 'score': 0.997}]
+```
+---
+## Label Schema
+| ID | Label | Description |
+|---|---|---|
+| 0 | safe | No vulnerability detected |
+| 1 | reentrancy | Reentrancy attack (DAO-style) |
+| 2 | integer_overflow | Arithmetic overflow / underflow |
+| 3 | access_control | Unprotected ownership or selfdestruct |
+| 4 | tx_origin_phishing | tx.origin used for authentication |
+| 5 | dos_gas | Unbounded loop / gas exhaustion |
+| 6 | unchecked_call | External call return value ignored |
+| 7 | front_running_mev | Mempool-visible state / TOD |
+| 8 | timestamp_dependence | block.timestamp manipulation |
+| 9 | proxy_storage_collision | Delegatecall storage slot collision |
+| 10 | flash_loan_oracle | Oracle price manipulation via flash loan |
+| 11 | flash_loan_single_block | Single-block liquidity attack |
+| 12 | misnamed_constructor | Pre-Solidity-0.5 constructor naming bug |
+| 13 | other | Multi-class or miscellaneous vulnerability |
+---
+## Training Data
+Assembled from four open-source sources using the [prepare_datasets.py](https://github.com/emekaphilian/TrustChainAI/blob/main/TrustChainAi/scripts/prepare_datasets.py) pipeline:
+| Source | Contracts |
+|---|---|
+| SmartBugs Curated | 143 |
+| SolidiFI Benchmark | 1,700 |
+| DeFiHackLabs | 729 |
+| Not-So-Smart Contracts | 25 |
+| Synthetic augmentation | 3,600 |
+| **Total (after dedup)** | **6,879** |
+Split: 70% train / 15% val / 15% test (stratified by label).
+---
 ## Training Details
+| Parameter | Value |
+|---|---|
+| Base model | microsoft/codebert-base |
+| Epochs | 5 (best checkpoint at epoch 2) |
+| Batch size | 16 |
+| Learning rate | 2e-5 |
+| Optimizer | AdamW (weight decay 0.01, warmup 100 steps) |
+| Max token length | 512 |
+| Mixed precision | fp16 |
+| Hardware | Google Colab T4 GPU |
+---
+## Intended Use
+- Pre-deployment security screening of Solidity smart contracts
+- Automated vulnerability triage for DeFi protocols
+- Research baseline for smart contract security ML benchmarks
+- Integration into the TrustChainAI multi-agent audit pipeline
+## Out-of-Scope Use
+- This model is **not a substitute** for a full professional security audit on high-value contracts
+- Performance on Vyper, Yul, or non-EVM contracts is untested
+- The `tx_origin_phishing` class has limited real training samples (28); treat predictions for this class with extra caution
+---
+## Limitations & Bias
+- Synthetic augmentation was used for 9 of 13 classes to compensate for dataset scarcity. Synthetic contracts may not fully capture real-world obfuscation patterns.
+- The `tx_origin_phishing` class had only 28 real-world training samples; model confidence for this class may be lower in practice.
+- Training data skews toward older Solidity vulnerability patterns (pre-0.8). Newer attack vectors may be underrepresented.
+---
+## Citation
+```bibtex
+@misc{trustchainai2025,
+  author = {Emeka Philian},
+  title  = {TrustChainAI: AI-Powered Smart Contract Auditor},
+  year   = {2025},
+  url    = {https://github.com/emekaphilian/TrustChainAI}
+}
+```
+---
+## Links
+- 🔗 GitHub: [emekaphilian/TrustChainAI](https://github.com/emekaphilian/TrustChainAI)
+- 🤗 Profile: [emekaphilians](https://huggingface.co/emekaphilians)
+- 📄 Architecture: [docs/ARCHITECTURE.md](https://github.com/emekaphilian/TrustChainAI/blob/main/docs/ARCHITECTURE.md)