turkish-code-detector

Model Card

A lightweight binary classifier that tells whether a Turkish input string is pure/partial code (CODE) or ordinary natural language (NL).
The model is designed as a guard-rail component in LLM pipelines:
if the user prompt is classified as CODE, upstream orchestration can refuse to forward it to the LLM, apply rate limits, or route it to a different policy.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

clf = pipeline("text-classification",
               model="yeniguno/turkish-code-detector",
               tokenizer="yeniguno/turkish-code-detector")

prompt = "def faktoriyel(n):\n    return 1 if n <= 1 else n * faktoriyel(n-1)"
result = clf(prompt)
print(f"Classification: {result}\n")
# Classification: [{'label': 'CODE', 'score': 0.999995231628418}]

prompt = "Linux'un yaratıcısı kimdir, biliyor musun?"
result = clf(prompt)
print(f"Classification: {result}\n")
# Classification: [{'label': 'NL', 'score': 0.9998611211776733}]

Intended Use & Limitations

The classifier was trained only on Turkish text + polyglot code snippets.
Unseen languages (e.g. Japanese text) may be mis-labelled NL.
Very short ambiguous strings (e.g. "int") can be mis-labelled CODE.

Training Data

Training Hyperparameters

Evaluation

All metrics computed with
id2label = {0: "NL", 1: "CODE"}.