Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,44 @@
+---
+license: other
+license_name: qtrust-research
+base_model: huggingface/codeberta-language-id
+tags:
+- cryptography
+- post-quantum-cryptography
+- code-classification
+- crypto-discovery
+metrics:
+- f1
+- precision
+- recall
+---
+
+# Q-Trust CryptoCodeDetector (CodeBERTa fine-tune)
+
+Fine-tuned crypto-usage discovery model from [Q-Trust](https://github.com/humoge7502/q-trust)
+(`qtrust_ai/` intelligence layer). Detects cryptographic API usage and algorithm families in
+source code — the discovery stage that feeds CBOM generation and PQC migration planning.
+
+## Training
+
+- **Corpus:** 13,973 real code files — SolidiFI, SmartBugs, EIPs, WebAuthn blockchain contracts, OSS crypto repos
+- **Schedule:** 4-epoch GPU fine-tune (A100), deterministic seed (same seed → same F1)
+- **Dataset:** [`KRPur/q-trust-datasets`](https://huggingface.co/datasets/KRPur/q-trust-datasets) (`code_corpus.json`)
+
+## Held-out results (repo-disjoint, n=2415)
+
+| Metric | Q-Trust ensemble | Rules-only | Majority | Random |
+|---|---|---|---|---|
+| **F1** | **0.9525** | 0.673 | 0.8683 | 0.5981 |
+| Precision | 0.952 | 0.979 | — | — |
+| Recall | 0.953 | 0.513 | 1.0 | — |
+
+Source: `qtrust_ai/artifacts/benchmark_comparison.json` (seed 42) in the GitHub repo.
+
+## Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+m = AutoModelForSequenceClassification.from_pretrained("KRPur/q-trust-codebert")
+t = AutoTokenizer.from_pretrained("KRPur/q-trust-codebert")
+```