Update README.md

README.md

@@ -18,6 +18,8 @@ repo_url: https://github.com/MatteoFasulo/clef2025-checkthat
 model-index:
 - name: mdeberta-v3-base-subjectivity-italian
   results: []
+datasets:
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 ---
 
 # mdeberta-v3-base-subjectivity-italian
@@ -100,70 +102,37 @@ The following hyperparameters were used during training:
 You can use this model for text classification (subjectivity detection) with the `transformers` library:
 
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model_name = "MatteoFasulo/mdeberta-v3-base-subjectivity-italian"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# Example 1: Subjective sentence
-text_1 = "This is a truly exceptional movie with stunning visuals and a captivating plot."
-inputs_1 = tokenizer(text_1, return_tensors="pt")
-
-with torch.no_grad():
-    logits_1 = model(**inputs_1).logits
-
-predicted_class_id_1 = logits_1.argmax().item()
-predicted_label_1 = model.config.id2label[predicted_class_id_1]
-
-print(f"Text: '{text_1}'")
-print(f"Predicted label: {predicted_label_1}") # Expected: SUBJ
-
-# Example 2: Objective sentence
-text_2 = "The capital of France is Paris."
-inputs_2 = tokenizer(text_2, return_tensors="pt")
-
-with torch.no_grad():
-    logits_2 = model(**inputs_2).logits
-
-predicted_class_id_2 = logits_2.argmax().item()
-predicted_label_2 = model.config.id2label[predicted_class_id_2]
-
-print(f"Text: '{text_2}'")
-print(f"Predicted label: {predicted_label_2}") # Expected: OBJ
-
-# Example 3: Batch processing
-texts_to_classify = [
-    "I believe this decision is a grave mistake for our future.",
-    "The report indicates a significant decline in quarterly earnings.",
-    "What an absolutely brilliant performance by the lead actor!",
-    "The meeting is scheduled for tomorrow at 10 AM in conference room B."
-]
-inputs_batch = tokenizer(texts_to_classify, padding=True, truncation=True, return_tensors="pt")
-
-with torch.no_grad():
-    logits_batch = model(**inputs_batch).logits
-
-predicted_class_ids_batch = logits_batch.argmax(dim=1).tolist()
-predicted_labels_batch = [model.config.id2label[id] for id in predicted_class_ids_batch]
-
-for text, label in zip(texts_to_classify, predicted_labels_batch):
-    print(f"Text: '{text}' -> Label: {label}")
+from transformers import pipeline
+
+# Load the text classification pipeline
+classifier = pipeline(
+    "text-classification",
+    model="MatteoFasulo/mdeberta-v3-base-subjectivity-italian",
+    tokenizer="microsoft/mdeberta-v3-base",
+)
+
+# Example usage:
+# A subjective sentence
+result_subj = classifier("Questa è una scoperta affascinante e fantastica!")
+print(f"Sentence: 'This is a truly amazing and groundbreaking discovery!' -> {result_subj}")
+
+# An objective sentence
+result_obj = classifier("In particolare Volkswagen e Stellantis son o arrivati a cedere il 7% in Borsa.")
+print(f"Sentence: 'The new policy will be implemented next quarter.' -> {result_obj}")
 ```
 
 ## Citation
 
-If you find this model or the associated work useful, please cite the original paper:
+If you find our work helpful or inspiring, please feel free to cite it:
 
 ```bibtex
-@misc{antoun2024camembert20smarterfrench,
-      title={AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles},
-      author={Wissam Antoun and Francis Kulumba and Rian Touchent and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
-      year={2024},
+@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},
+      url={https://arxiv.org/abs/2507.11764}, 
 }
 ```