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
Update README.md
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README.md
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@@ -340,10 +340,15 @@ If you use this model, please cite our paper:
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```bibtex
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@article{mecellem2026,
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title={Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain},
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author={Uğur, Özgür and Göksu, Mahmut and
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journal={
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year={2026},
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}
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```
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### Base Model References
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```bibtex
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@article{mecellem2026,
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title={Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain},
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author={Uğur, Özgür and Göksu, Mahmut and Çimen, Mahmut and Yılmaz, Musa and Şavirdi, Esra and Demir, Alp Talha and Güllüce, Rumeysa and Çetin, İclal and Sağbaş, Ömer Can},
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journal={arXiv preprint arXiv:2601.16018},
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year={2026},
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month={January},
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url={https://arxiv.org/abs/2601.16018},
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doi={10.48550/arXiv.2601.16018},
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eprint={2601.16018},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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### Base Model References
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