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
Fine-tuned Qwen 0.6B on Pazarama triplets for e-commerce hard negative disambiguation.
cc07baf verified | { | |
| "additional_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>", | |
| "<|object_ref_start|>", | |
| "<|object_ref_end|>", | |
| "<|box_start|>", | |
| "<|box_end|>", | |
| "<|quad_start|>", | |
| "<|quad_end|>", | |
| "<|vision_start|>", | |
| "<|vision_end|>", | |
| "<|vision_pad|>", | |
| "<|image_pad|>", | |
| "<|video_pad|>" | |
| ], | |
| "eos_token": { | |
| "content": "<|im_end|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |