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--- |
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language: "code" |
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license: "mit" |
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tags: |
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- dockerfile |
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- hadolint |
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- binary-classification |
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- codebert |
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model-index: |
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- name: Binary Dockerfile Classifier |
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results: [] |
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--- |
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# 🧱 Dockerfile Quality Classifier – Binary Model |
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This model predicts whether a given Dockerfile is: |
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- ✅ **GOOD** – clean and adheres to best practices (no top rule violations) |
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- ❌ **BAD** – violates at least one important rule (from Hadolint) |
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It is the first step in a full ML-based Dockerfile linter. |
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--- |
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## 🧠 Model Overview |
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- **Architecture:** Fine-tuned `microsoft/codebert-base` |
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- **Task:** Binary classification (`good` vs `bad`) |
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- **Input:** Full Dockerfile content as plain text |
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- **Output:** `[prob_good, prob_bad]` — softmax scores |
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- **Max input length:** 512 tokens |
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--- |
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## 📚 Training Details |
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- **Data source:** Real-world and synthetic Dockerfiles |
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- **Labels:** Based on [Hadolint](https://github.com/hadolint/hadolint) top 30 rules |
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- **Bad examples:** At least one rule violated |
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- **Good examples:** Fully clean files |
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- **Dataset balance:** 15000 BAD / 1500 GOOD (clean) |
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--- |
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## 🧪 Evaluation Results |
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Evaluation on a held-out test set of 1,650 Dockerfiles: |
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| Class | Precision | Recall | F1-score | Support | |
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|-------|-----------|--------|----------|---------| |
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| good | 0.96 | 0.91 | 0.93 | 150 | |
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| bad | 0.99 | 1.00 | 0.99 | 1500 | |
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| **Accuracy** | | | **0.99** | 1650 | |
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--- |
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## 🚀 Quick Start |
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### 🧪 Step 1 — Create test script |
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Save this as `test_binary_predict.py`: |
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```python |
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import sys |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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from pathlib import Path |
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path = Path(sys.argv[1]) |
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text = path.read_text(encoding="utf-8") |
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tokenizer = AutoTokenizer.from_pretrained("LeeSek/binary-dockerfile-model") |
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model = AutoModelForSequenceClassification.from_pretrained("LeeSek/binary-dockerfile-model") |
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model.eval() |
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inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=512) |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze() |
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label = "GOOD" if torch.argmax(probs).item() == 0 else "BAD" |
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print(f"Prediction: {label} — Probabilities: good={probs[0]:.3f}, bad={probs[1]:.3f}") |
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``` |
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--- |
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### 📄 Step 2 — Create good and bad Dockerfile |
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Good: |
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```docker |
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FROM node:18 |
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WORKDIR /app |
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COPY . . |
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RUN npm install |
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CMD ["node", "index.js"] |
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``` |
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Bad: |
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```docker |
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FROM ubuntu:latest |
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RUN apt-get install python3 |
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ADD . /app |
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WORKDIR /app |
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RUN pip install flask |
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CMD python3 app.py |
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``` |
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--- |
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### ▶️ Step 3 — Run the prediction |
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```bash |
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python test_binary_predict.py Dockerfile |
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``` |
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Expected output: |
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``` |
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Prediction: GOOD — Probabilities: good=0.998, bad=0.002 |
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``` |
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--- |
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## 🗂 Extras |
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The full training and evaluation pipeline — including data preparation, training, validation, prediction — is available in the **`scripts/`** folder. |
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> 💬 **Note:** Scripts are written with **Polish comments and variable names** for clarity during local development. Logic is fully portable. |
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--- |
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## 📘 License |
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MIT |
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--- |
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## 🙌 Credits |
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- Model powered by [Hugging Face Transformers](https://huggingface.co/transformers) |
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- Tokenizer: CodeBERT |
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- Rule definitions: [Hadolint](https://github.com/hadolint/hadolint) |
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