Add library_name and update pipeline_tag

README.md

@@ -1,8 +1,11 @@
 ---
+base_model: ModernBERT-large
 language:
 - tr
 - en
 license: apache-2.0
+pipeline_tag: feature-extraction
+library_name: transformers
 tags:
 - fill-mask
 - turkish
@@ -12,12 +15,12 @@ tags:
 - modernbert
 - TRUBA
 - MN5
-base_model: ModernBERT-large
-pipeline_tag: fill-mask
 ---
 
 # Mursit-Large
 
+This model was introduced in the paper [Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain](https://huggingface.co/papers/2601.16018).
+
 [![GitHub](https://img.shields.io/badge/GitHub-NewMindAI-black?logo=github)](https://github.com/newmindai/mecellem-models) [![HuggingFace Space](https://img.shields.io/badge/HF%20Space-Mizan-blue?logo=huggingface)](https://huggingface.co/spaces/newmindai/Mizan) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 
 ## Model Description
@@ -85,7 +88,7 @@ The following table presents MLM accuracy scores (averaged across the 80-10-10 s
 | KocLab-Bilkent/BERTurk-Legal | 54.10 |
 | ytu-ce-cosmos/turkish-base-bert-uncased | 52.69 |
 
-*MLM accuracy averaged across the 80-10-10 masking strategy. turkish-base-bert-uncased was evaluated only on uncased datasets. Evaluation datasets: blackerx/turkish_v2, fthbrmnby/turkish_product_reviews, hazal/Turkish-Biomedical-corpus-trM, newmindai/EuroHPC-Legal. All experiments are reproducible (see Section A.2 in the paper).*
+*MLM accuracy averaged across the 80-10-10 masking strategy. Evaluation datasets: blackerx/turkish_v2, fthbrmnby/turkish_product_reviews, hazal/Turkish-Biomedical-corpus-trM, newmindai/EuroHPC-Legal. All experiments are reproducible (see Section A.2 in the paper).*
 
 ## Performance on MTEB-Turkish Benchmark
 
@@ -178,6 +181,7 @@ with torch.no_grad():
         score = predictions[0][idx].item()
         print(f"{token}: {score:.4f}")
 ```
+
 # ONNX Model Inference - Masked Language Modeling (MLM)
 
 This script demonstrates how to use the ONNX model from Hugging Face for masked language modeling tasks.
@@ -253,12 +257,6 @@ for p in predictions:
 - Question answering
 - Feature extraction for downstream tasks
 
-## Reproducibility
-
-To reproduce the MLM benchmark results for this model, please refer to:
-
-- **MLM Benchmark Results:** [github.com/newmindai/mecellem-models/benchmark/mlm](https://github.com/newmindai/mecellem-models/tree/main/benchmark/mlm) - Contains code and evaluation configurations for reproducing MLM accuracy scores on Turkish datasets using the 80-10-10 masking strategy.
-
 ## Acknowledgments
 
 This work was supported by the EuroHPC Joint Undertaking through project etur46 with access to the MareNostrum 5 supercomputer, hosted by Barcelona Supercomputing Center (BSC), Spain. MareNostrum 5 is owned by EuroHPC JU and operated by BSC. We are grateful to the BSC support team for their assistance with job scheduling, environment configuration, and technical guidance throughout the project.
@@ -272,7 +270,7 @@ If you use this model, please cite our paper:
 ```bibtex
 @article{mecellem2026,
   title={Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain},
-  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},
+  author={Uğur, Özgür and Göksu, Mahmut and Çimen, Mahmut and Yılmaz, Musa and Şavirdi, Esra and Demir, Alp Talha and Güllüce, Rumeysa and İclal Çetin and Ömer Can Sağbaş},
   journal={arXiv preprint arXiv:2601.16018},
   year={2026},
   month={January},
@@ -283,6 +281,7 @@ If you use this model, please cite our paper:
   primaryClass={cs.CL}
 }
 ```
+
 ### Base Model References
 
 ```bibtex
@@ -292,6 +291,4 @@ If you use this model, please cite our paper:
   booktitle={Proceedings of the 2025 Conference on Language Models},
   year={2025}
 }
-```
-
-<!-- Updated: 2026-01-15 09:38:24 -->
+```