Instructions to use fkuyumcu/turkish-embedding-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fkuyumcu/turkish-embedding-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fkuyumcu/turkish-embedding-v3") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Turkish Embedding Model
Bu model, Gemma-3-4b-it modelinin embedding katmanı RAG amaçlı olarak fine-tune edilmiştir. Türkçe soru-cevap çiftlerini vektörize etmek için kullanılabilir.
Kullanım
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('your-username/turkish-embedding-v3')
# Cümleleri kodlama
sentences = ['Bu bir örnek cümledir.', 'Türkçe doğal dil işleme']
embeddings = model.encode(sentences)
Eğitim Detayları
- Baz model: google/gemma-3-4b-it
- Eğitim veri seti: WikiRAG-TR
- Loss: Cosine Embedding Loss
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