Sentence Similarity
sentence-transformers
ONNX
Safetensors
Turkish
English
modernbert
feature-extraction
information-retrieval
dense-retrieval
turkish
legal
turkish-legal
mecellem
TRUBA
MN5
text-embeddings-inference
Instructions to use newmindai/Mursit-Base-TR-Retrieval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use newmindai/Mursit-Base-TR-Retrieval with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("newmindai/Mursit-Base-TR-Retrieval") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Inference
- Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 8543e7f32a265008563c3e0faea3f4d77fd584afafee8ff0b558bee45d10e120
- Size of remote file:
- 131 kB
- SHA256:
- 5a78c6de7930175c191dc8d15356f0215b54200aa178e23d2e3bf19fec6cc9b2
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