Update README.md

README.md

@@ -27,7 +27,7 @@ language:
 - ro
 - uk
 datasets:
-- clef-2025-checkthat-task1
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 arxiv_id: 2507.11764
 code_url: https://github.com/MatteoFasulo/clef2025-checkthat
 ---
@@ -87,25 +87,21 @@ You can easily use this model with the Hugging Face `transformers` library:
 ```python
 from transformers import pipeline
 
-# Load the text classification pipeline with the model
+# Load the text classification pipeline
 classifier = pipeline(
     "text-classification",
-    model="MatteoFasulo/mdeberta-v3-base-subjectivity-multilingual"
+    model="MatteoFasulo/mdeberta-v3-base-subjectivity-multilingual",
+    tokenizer="microsoft/mdeberta-v3-base",
 )
 
-# Example 1: Objective sentence
-text_objective = "The capital of France is Paris."
-result_objective = classifier(text_objective)
-print(f"Text: '{text_objective}'
-Result: {result_objective}")
-# Expected output: [{'label': 'OBJ', 'score': <confidence_score>}]
-
-# Example 2: Subjective sentence
-text_subjective = "This is a fantastic movie! I absolutely loved it."
-result_subjective = classifier(text_subjective)
-print(f"Text: '{text_subjective}'
-Result: {result_subjective}")
-# Expected output: [{'label': 'SUBJ', 'score': <confidence_score>}]
+# Example usage:
+result1 = classifier("Questa è una scoperta affascinante e fantastica!")
+print(f"Classification: {result1}")
+# Expected output: [{'label': 'SUBJ', 'score': ...}]
+
+result2 = classifier("The capital of France is Paris.")
+print(f"Classification: {result2}")
+# Expected output: [{'label': 'OBJ', 'score': ...}]
 ```
 
 ## Training procedure
@@ -146,13 +142,16 @@ The code and materials for this model are available on GitHub: [MatteoFasulo/cle
 
 ## Citation
 
-If you find this work useful for your research, please 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 Zhumash, Alim M Z and Turchi, Matteo and Rossi, Andrea and Di Nunzio, Giorgio Maria},
-  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}, 
 }
 ```