Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
Turkish
English
modernbert
fill-mask
turkish
legal
turkish-legal
mecellem
TRUBA
MN5
text-embeddings-inference
Instructions to use newmindai/Mursit-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use newmindai/Mursit-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="newmindai/Mursit-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("newmindai/Mursit-Base") model = AutoModelForMaskedLM.from_pretrained("newmindai/Mursit-Base") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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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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### Base Model References
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booktitle={Proceedings of the 2025 Conference on Language Models},
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year={2025}
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}
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```
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<!-- Updated: 2026-01-15 09:38:13 -->
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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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booktitle={Proceedings of the 2025 Conference on Language Models},
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year={2025}
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}
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```
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