Upload README.md

README.md

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+# 🧱 Dockerfile Quality Classifier – Binary Model
+
+This model predicts whether a given Dockerfile is:
+
+- ✅ **GOOD** – clean and adheres to best practices (no top rule violations)
+- ❌ **BAD** – violates at least one important rule (from Hadolint)
+
+It is the first step in a full ML-based Dockerfile linter.
+
+---
+
+## 🧠 Model Overview
+
+- **Architecture:** Fine-tuned `microsoft/codebert-base`
+- **Task:** Binary classification (`good` vs `bad`)
+- **Input:** Full Dockerfile content as plain text
+- **Output:** `[prob_good, prob_bad]` — softmax scores
+- **Max input length:** 512 tokens
+
+---
+
+## 📚 Training Details
+
+- **Data source:** Real-world and synthetic Dockerfiles
+- **Labels:** Based on [Hadolint](https://github.com/hadolint/hadolint) top 30 rules
+- **Bad examples:** At least one rule violated
+- **Good examples:** Fully clean files
+- **Dataset balance:** 50/50
+
+---
+
+## 🧪 Evaluation Results
+
+Evaluation on a held-out test set of 1,650 Dockerfiles:
+
+| Class | Precision | Recall | F1-score | Support |
+|-------|-----------|--------|----------|---------|
+| good  | 0.96      | 0.91   | 0.93     | 150     |
+| bad   | 0.99      | 1.00   | 0.99     | 1500    |
+| **Accuracy** |       |        | **0.99** | 1650    |
+
+---
+
+## 🚀 Quick Start
+
+### 🧪 Step 1 — Create test script
+
+Save this as `test_binary_predict.py`:
+
+```python
+import sys
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from pathlib import Path
+
+path = Path(sys.argv[1])
+text = path.read_text(encoding="utf-8")
+
+tokenizer = AutoTokenizer.from_pretrained("LeeSek/binary-dockerfile-model")
+model = AutoModelForSequenceClassification.from_pretrained("LeeSek/binary-dockerfile-model")
+model.eval()
+
+inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=512)
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+    probs = torch.nn.functional.softmax(logits, dim=1).squeeze()
+
+label = "GOOD" if torch.argmax(probs).item() == 0 else "BAD"
+print(f"Prediction: {label} — Probabilities: good={probs[0]:.3f}, bad={probs[1]:.3f}")
+```
+
+---
+
+### 📄 Step 2 — Create a test Dockerfile
+
+Save the following as `Dockerfile`:
+
+```dockerfile
+FROM node:18
+WORKDIR /app
+COPY . .
+RUN npm install
+CMD ["node", "index.js"]
+```
+
+---
+
+### ▶️ Step 3 — Run the prediction
+
+```bash
+python test_binary_predict.py Dockerfile
+```
+
+Expected output:
+
+```
+Prediction: GOOD — Probabilities: good=0.998, bad=0.002
+```
+
+---
+
+## 📘 License
+
+MIT
+
+---
+
+## 🙌 Credits
+
+- Model powered by [Hugging Face Transformers](https://huggingface.co/transformers)
+- Tokenizer: CodeBERT
+- Rule definitions: [Hadolint](https://github.com/hadolint/hadolint)