Update README.md

README.md

@@ -17,6 +17,8 @@ pipeline_tag: text-classification
 model-index:
 - name: mdeberta-v3-base-subjectivity-arabic
   results: []
+datasets:
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 ---
 
 # mdeberta-v3-base-subjectivity-arabic
@@ -83,37 +85,43 @@ The following hyperparameters were used during training:
 You can use the model directly with the `transformers` library for text classification:
 
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model_name = "MatteoFasulo/mdeberta-v3-base-subjectivity-arabic"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-text = "This is a very subjective opinion."
-inputs = tokenizer(text, return_tensors="pt")
-
-with torch.no_grad():
-    logits = model(**inputs).logits
-
-predicted_class_id = logits.argmax().item()
-print(model.config.id2label[predicted_class_id])
+from transformers import pipeline
+
+# Load the text classification pipeline
+classifier = pipeline(
+    "text-classification",
+    model="MatteoFasulo/mdeberta-v3-base-subjectivity-arabic",
+    tokenizer="microsoft/mdeberta-v3-base",
+)
+
+# Example usage for an objective sentence
+text1 = "وهكذا بدأت النساء يعين أهمية دورهن في عدم الصمت أمام هذه الاقتحامات ورفضها بإعلاء صيحات الله أكبر."
+result1 = classifier(text1)
+print(f"Text: '{text1}' Classification: {result1}")
+# Expected output: [{'label': 'OBJ', 'score': ...}]
+
+# Example usage for a subjective sentence
+text2 = "ستشمل الشحنة الأولية نصف الجرعات، يليها النصف الثاني بعد ثلاثة أسابيع."
+result2 = classifier(text2)
+print(f"Text: '{text2}' Classification: {result2}")
+# Expected output: [{'label': 'SUBJ', 'score': ...}]
 ```
 
 ## Code
 The official code and materials for this project are available on GitHub: [https://github.com/MatteoFasulo/clef2025-checkthat](https://github.com/MatteoFasulo/clef2025-checkthat).
 
 ## Citation
-If you find our work helpful or inspiring, please feel free to cite it.
+
+If you find our work helpful or inspiring, please feel free to cite it:
 
 ```bibtex
-@misc{antoun2024camembert20smarterfrench,
-      title={CamemBERT 2.0: A Smarter French Language Model Aged to Perfection},
-      author={Wissam Antoun and Francis Kulumba and Rian Touchent and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
-      year={2024},
-      eprint={2411.08868},
+@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/2411.08868},
+      url={https://arxiv.org/abs/2507.11764}, 
 }
 ```