+---------------------+--------+ | Metrik | Değer | +---------------------+--------+ | ROUGE-1 | 0.2439 | | ROUGE-2 | 0.1303 | | ROUGE-L | 0.2147 | | BLEU | 0.0406 | | METEOR | 0.2262 | | BERTScore Precision | 0.5286 | | BERTScore Recall | 0.5834 | | BERTScore F1 | 0.553 | +---------------------+--------+
✅ Model evaluation is complete and all results are logged to wandb.
Model Card for SmolLM2-Ziraat-Turkish-v1
🧠 Model Summary
SmolLM2-Ziraat-Turkish-v1 is a fine-tuned version of the unsloth/SmolLM2-1.7B model, trained using Unsloth and PEFT (Parameter-Efficient Fine-Tuning). This model has been tailored for Turkish language tasks with a focus on agriculture, finance, and general-purpose conversation.
🇹🇷 Model Özeti
SmolLM2-Ziraat-Turkish-v1, unsloth/SmolLM2-1.7B tabanlı bir model olup, Unsloth ve PEFT (Parameter-Efficient Fine-Tuning) yöntemleriyle Türkçe diline yönelik olarak eğitilmiştir. Tarım, finans ve genel sohbet amaçlı kullanım senaryoları için optimize edilmiştir.
🔍 Model Details / Model Detayları
- Developed by / Geliştiren: hosmankarabulut
- Model type / Model türü: Causal Language Model (AutoRegressive)
- Language / Dil: Turkish (Türkçe)
- License / Lisans: apache-2.0
- Fine-tuned with / Eğitim Aracı: Unsloth + PEFT
- Base model / Taban model: unsloth/SmolLM2-1.7B
🔗 Sources / Kaynaklar
- Model Repository / Model Deposu: https://huggingface.co/hosmankarabulut/SmolLM2-Ziraat-Turkish-v1
- Base model / Taban model: unsloth/SmolLM2-1.7B
✅ Intended Uses / Amaçlanan Kullanım
- Turkish chatbots, Q&A systems
- Agricultural and financial assistants
- General-purpose Turkish LLMs
🚫 Out-of-Scope Use / Uygun Olmayan Kullanım
- Medical, legal, or high-risk decision-making
- Misinformation or unethical applications
⚠️ Bias, Risks and Limitations / Önyargılar, Riskler ve Sınırlamalar
Model may still contain biases inherited from the base model. Performance is best within Turkish language and domain-specific contexts (agriculture, finance).
🧪 Training & Evaluation / Eğitim ve Değerlendirme
- Training Library / Eğitim Kütüphanesi: Unsloth
- Hardware Used / Kullanılan Donanım: RTX 3090
- Precision: bf16 (mixed precision)
- Dataset: Özel Türkçe veriseti (tarım odaklı)
- Evaluation Tool:
wandb(Weights & Biases)
📊 Evaluation Results / Değerlendirme Sonuçları
| Metric | Value |
|---|---|
| ROUGE-1 | 0.2439 |
| ROUGE-2 | 0.1303 |
| ROUGE-L | 0.2147 |
| BLEU | 0.0406 |
| METEOR | 0.2262 |
| BERTScore Precision | 0.5286 |
| BERTScore Recall | 0.5834 |
| BERTScore F1 | 0.553 |
✅ Tüm metrikler başarıyla hesaplandı ve wandb üzerinde kaydedildi.
💡 Quickstart / Hızlı Başlangıç
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained("hosmankarabulut/SmolLM2-Ziraat-Turkish-v1", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("hosmankarabulut/SmolLM2-Ziraat-Turkish-v1")
inputs = tokenizer("Türkiye'de tarım politikaları hakkında ne düşünüyorsun?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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