Sentence Similarity
sentence-transformers
Safetensors
Turkish
xlm-roberta
feature-extraction
turkish
turkish-literature
bge-m3
text-embeddings-inference
Instructions to use yusufekorman/turkce-edebiyat-embedding-bge-m3-basic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yusufekorman/turkce-edebiyat-embedding-bge-m3-basic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yusufekorman/turkce-edebiyat-embedding-bge-m3-basic") sentences = [ "Deniz, dalgaların kıyıya vuruşuyla huzur veren bir ses çıkarıyordu.", "Dalgaların sahile çarptığında çıkardığı ses, insana dinginlik veriyordu.", "Bugün marketten alışveriş yapmam gerekiyor, akşama misafir gelecek." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "embedding_dimension": 1024, | |
| "pooling_mode": "cls", | |
| "include_prompt": true | |
| } |