Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:13842
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use mertcobanov/mpnet-base-all-nli-triplet-turkish-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mertcobanov/mpnet-base-all-nli-triplet-turkish-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mertcobanov/mpnet-base-all-nli-triplet-turkish-v3") sentences = [ "Bir adam bir elinde kahve fincanı, diğer elinde tuvalet fırçası ile tuvaletin önünde duruyor.", "Şef ve orkestra oturmuyor.", "Bir adam bir banyoda duruyor.", "Bir adam kahve demlemeye çalışıyor." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle