Sentence Similarity
sentence-transformers
ONNX
Safetensors
Turkish
English
qwen3
feature-extraction
information-retrieval
dense-retrieval
turkish
legal
turkish-legal
mecellem
qwen
decoder-to-encoder
clm-embedding
TRUBA
MN5
text-embeddings-inference
Instructions to use newmindai/Mursit-Embed-Qwen3-1.7B-TR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use newmindai/Mursit-Embed-Qwen3-1.7B-TR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("newmindai/Mursit-Embed-Qwen3-1.7B-TR") 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] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f64909b808292db1df8ecfaf17181d7aa5d7a763980fd092a836a74a4ced76a4
- Size of remote file:
- 11.4 MB
- SHA256:
- 4b307b946f2be5ff795920d27809e854907e496243282459c3760c7bcdfe3307
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