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
File size: 429 Bytes
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{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
},
{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
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