Update README.md

README.md

@@ -17,6 +17,8 @@ tags:
 model-index:
 - name: mdeberta-v3-base-subjectivity-multilingual-no-arabic
   results: []
+datasets:
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 ---
 
 # mdeberta-v3-base-subjectivity-multilingual-no-arabic
@@ -90,9 +92,12 @@ You can use the model with the `pipeline` API from the `transformers` library fo
 ```python
 from transformers import pipeline
 
-model_name = "MatteoFasulo/mdeberta-v3-base-subjectivity-multilingual-no-arabic"
-# The pipeline automatically infers the task and labels from the model config
-classifier = pipeline("text-classification", model=model_name)
+# Load the text classification pipeline
+classifier = pipeline(
+    "text-classification",
+    model="MatteoFasulo/mdeberta-v3-base-subjectivity-multilingual-no-arabic",
+    tokenizer="microsoft/mdeberta-v3-base",
+)
 
 # Example usage:
 # A subjective sentence
@@ -106,14 +111,17 @@ print(f"Sentence: 'The new policy will be implemented next quarter.' -> {result_
 
 ## Citation
 
-If you find our work helpful or inspiring, please feel free to cite the paper:
+If you find our work helpful or inspiring, please feel free to cite it:
 
 ```bibtex
-@article{fasulo2025ai,
-  title={AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles},
-  author={Fasulo, Matteo and Fabris, Alessandro and Caldararu, Silvia and Kiselov, Valerij and Stoica, George and Ilie, Andrei},
-  journal={arXiv preprint arXiv:2507.11764},
-  year={2025}
+@misc{fasulo2025aiwizardscheckthat2025,
+      title={AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles}, 
+      author={Matteo Fasulo and Luca Babboni and Luca Tedeschini},
+      year={2025},
+      eprint={2507.11764},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.11764}, 
 }
 ```