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
- Xet hash:
- 7f686e947e0b9d36c958512a852baddaabd2b3ff5be7abad2fb11a1fb2755fe1
- Size of remote file:
- 17.1 MB
- SHA256:
- a514807cffabd8abaf028cfaffe7ff0c4f60b97ea2db80c41f14172ae6b018ca
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