Update README.md

README.md

@@ -16,6 +16,8 @@ tags:
 model-index:
 - name: mdeberta-v3-base-subjectivity-german
   results: []
+datasets:
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -58,32 +60,27 @@ The model was trained and evaluated on datasets provided for the **CLEF 2025 Che
 You can use this model directly with the Hugging Face `transformers` library for text classification:
 
 ```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
+from transformers import pipeline
+
+# Load the text classification pipeline
+classifier = pipeline(
+    "text-classification",
+    model="MatteoFasulo/mdeberta-v3-base-subjectivity-german",
+    tokenizer="microsoft/mdeberta-v3-base",
+)
+
+# Example usage for an objective sentence
+text1 = "Das Unternehmen meldete im letzten Quartal einen Gewinnanstieg von 10 %."
+result1 = classifier(text1)
+print(f"Text: '{text1}' Classification: {result1}")
+# Expected output: [{'label': 'OBJ', 'score': ...}]
+
+# Example usage for a subjective sentence
+text2 = "Dieses Produkt ist absolut erstaunlich und jeder sollte es ausprobieren!"
+result2 = classifier(text2)
+print(f"Text: '{text2}' Classification: {result2}")
+# Expected output: [{'label': 'SUBJ', 'score': ...}]
 
-model_name = "MatteoFasulo/mdeberta-v3-base-subjectivity-german"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# Example usage for an objective sentence:
-text_objective = "Der Bundeskanzler traf sich heute mit dem französischen Präsidenten." # The Chancellor met the French President today.
-inputs_obj = tokenizer(text_objective, return_tensors="pt")
-
-with torch.no_grad():
-    logits_obj = model(**inputs_obj).logits
-
-predicted_class_id_obj = logits_obj.argmax().item()
-print(f"'{text_objective}' is classified as: {model.config.id2label[predicted_class_id_obj]}")
-
-# Example usage for a subjective sentence:
-text_subjective = "Ich denke, dass diese Entscheidung eine Katastrophe ist." # I think that this decision is a disaster.
-inputs_subj = tokenizer(text_subjective, return_tensors="pt")
-
-with torch.no_grad():
-    logits_subj = model(**inputs_subj).logits
-
-predicted_class_id_subj = logits_subj.argmax().item()
-print(f"'{text_subjective}' is classified as: {model.config.id2label[predicted_class_id_subj]}")
 ```
 
 ## Training procedure
@@ -119,13 +116,17 @@ The following hyperparameters were used during training:
 - Tokenizers 0.21.0
 
 ## Citation
+
 If you find our work helpful or inspiring, please feel free to cite it:
+
 ```bibtex
-@article{aiwizards2025checkthat,
-  title={AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles},
-  author={Antoun, Wissam and Kulumba, Francis and Touchent, Rian and de la Clergerie, Éric and Sagot, Benoît and Seddah, Djamé},
-  journal={arXiv preprint arXiv:2507.11764},
-  year={2025},
-  url={https://arxiv.org/abs/2507.11764}
+@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}, 
 }
 ```