Sentence Similarity
sentence-transformers
Safetensors
English
Turkish
mpnet
feature-extraction
Generated from Trainer
dataset_size:120781
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use mertcobanov/mpnet-base-all-nli-triplet-turkish-v4-dgx 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-v4-dgx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mertcobanov/mpnet-base-all-nli-triplet-turkish-v4-dgx") sentences = [ "Bir köpek sahibi, evcil hayvanıyla birlikte koşuyor ve evcil hayvan bir parkurda engellerden kaçınıyor.", "Bazı bitkilerin önünde mavi bir kano.", "Bir adam köpeğinin yanında koşuyor.", "Adam bir kediyle birlikte." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5078f80742baa033188c416bb29ff713ddfde9e3754b62ec5afbddca978097be
- Size of remote file:
- 438 MB
- SHA256:
- b8aade3946a641859b60c68b877ff2dd0693f9a0184c3f64e2f98f87e526355d
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