Sentence Similarity
sentence-transformers
Safetensors
qwen3
feature-extraction
dense
Generated from Trainer
dataset_size:72985
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use turanyigitpazarama/qwen-0.6B-turkish-ecommerce-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use turanyigitpazarama/qwen-0.6B-turkish-ecommerce-tuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("turanyigitpazarama/qwen-0.6B-turkish-ecommerce-tuned") sentences = [ "kablosuz klavye mouse set", "Dexim KM-317 Slim DKM004 Kablosuz Klavye Mouse Set", "EXEP BASİC BEYAZ 200 3 LÜ ANKASTRE SET (ED400-EO320-EF200)", "Samsung Galaxy A17 128 GB 4 GB Siyah Cep Telefonu" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "prompts": { | |
| "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine", | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.1.1", | |
| "transformers": "5.2.0", | |
| "pytorch": "2.10.0+cu128" | |
| } | |
| } |