🧱 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 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:

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:

FROM node:18
WORKDIR /app
COPY . .
RUN npm install
CMD ["node", "index.js"]

Bad:

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

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
Tokenizer: CodeBERT
Rule definitions: Hadolint

LeeSek
/

binary-dockerfile-model