yeniguno
/

turkish-code-detector

@@ -1,199 +1,91 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- text-classification
+- code-classification
+- code-detection
+license: apache-2.0
+language:
+- tr
+base_model:
+- dbmdz/electra-base-turkish-mc4-uncased-discriminator
+pipeline_tag: text-classification
 ---
+## 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.
+```python
+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
+| ✓ Recommended                     | ✗ Not a Good Fit                          |
+|-----------------------------------|-------------------------------------------|
+| Prompt filtering in LLM stacks    | Detecting specific programming languages  |
+| Pre-screening user inputs in chat | Judging code quality or style            |
+| Moderating public text fields     | Detecting tiny inline code tokens in very long documents |
+| Fast, low-latency inference (≈1 ms on GPU) | Multilingual detection outside Turkish |
+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
+| Split | Total | **CODE** | **NL** |
+|-------|------:|---------:|-------:|
+| Train | **316 732** | 251 518 | 65 214 |
+| Dev   | 39 591 | 31 439 | 8 152 |
+| Test  | 39 592 | 31 440 | 8 152 |
+### Training Hyperparameters
+| Setting | Value |
+|---------|-------|
+| Optimiser | AdamW |
+| Effective batch | 32 (2 × 16, fp16) |
+| LR scheduler | linear-decay, warm-up 0 |
+| Max length | 256 tokens |
+| Epochs | ≤ 10 (early-stopping at 6 k steps ≈ 0.30 epoch) |
+| Loss | **Cross-entropy with *reversed* class weights**<br>`weight_NL = 10.0`  `weight_CODE = 1.0` |
+| Label smoothing | 0.1 |
+| Hardware | 1 × A100 40 GB (Google Colab) |
 ## Evaluation
+| Split | Acc | Prec | Recall | F1 |
+|-------|----:|-----:|-------:|---:|
+| Train | 0.9960 | 0.9978 | 0.9827 | 0.9902 |
+| Dev   | 0.9957 | 0.9981 | 0.9807 | 0.9894 |
+| Test  | 0.9954 | 0.9968 | 0.9807 | 0.9887 |
+All metrics computed with
+`id2label = {0: "NL", 1: "CODE"}`.