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---
language: "code"
license: "mit"
tags:
- dockerfile
- hadolint
- binary-classification
- codebert
model-index:
- name: Binary Dockerfile Classifier
results: []
---
# 🧱 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:** 15000 BAD / 1500 GOOD (clean)
---
## 🧪 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 good and bad Dockerfile
Good:
```docker
FROM node:18
WORKDIR /app
COPY . .
RUN npm install
CMD ["node", "index.js"]
```
Bad:
```docker
FROM ubuntu:latest
RUN apt-get install python3
ADD . /app
WORKDIR /app
RUN pip install flask
CMD python3 app.py
```
---
### ▶️ Step 3 — Run the prediction
```bash
python test_binary_predict.py Dockerfile
```
Expected output:
```
Prediction: GOOD — Probabilities: good=0.998, bad=0.002
```
---
## 🗂 Extras
The full training and evaluation pipeline — including data preparation, training, validation, prediction — is available in the **`scripts/`** folder.
> 💬 **Note:** Scripts are written with **Polish comments and variable names** for clarity during local development. Logic is fully portable.
---
## 📘 License
MIT
---
## 🙌 Credits
- Model powered by [Hugging Face Transformers](https://huggingface.co/transformers)
- Tokenizer: CodeBERT
- Rule definitions: [Hadolint](https://github.com/hadolint/hadolint)
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