Improve model card: Add pipeline tag, update license, GitHub link, and detailed sections

README.md

@@ -1,17 +1,23 @@
 ---
-library_name: transformers
-license: mit
 base_model: microsoft/mdeberta-v3-base
-tags:
-- generated_from_trainer
+language:
+- de
+library_name: transformers
+license: cc-by-4.0
 metrics:
 - accuracy
 - f1
+pipeline_tag: text-classification
+tags:
+- generated_from_trainer
+- subjectivity-detection
+- mdeberta-v3
+- sentiment
 model-index:
 - name: mdeberta-v3-base-subjectivity-german
   results: []
-language:
-- de
+datasets:
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -19,7 +25,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 # mdeberta-v3-base-subjectivity-german
 
-This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the [CheckThat! Lab Task 1 Subjectivity Detection at CLEF 2025](arxiv.org/abs/2507.11764).
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) for **Subjectivity Detection in News Articles**, as presented in the paper [AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles](https://arxiv.org/abs/2507.11764).
+
+It was developed as part of AI Wizards' participation in the CLEF 2025 CheckThat! Lab Task 1.
+
+**Code:** [https://github.com/MatteoFasulo/clef2025-checkthat](https://github.com/MatteoFasulo/clef2025-checkthat)
+
 It achieves the following results on the evaluation set:
 - Loss: 0.5760
 - Macro F1: 0.7720
@@ -32,15 +43,45 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is designed to classify sentences in news articles as either **subjective** (e.g., opinion-laden) or **objective**. This is a key component in combating misinformation, improving fact-checking pipelines, and supporting journalists. It is based on the `mDeBERTaV3-base` architecture and enhances transformer-based classifiers by integrating sentiment scores, derived from an auxiliary model, with sentence representations. This sentiment-augmented architecture, combined with robust decision threshold calibration to address class imbalance, significantly boosts performance, especially for the subjective F1 score.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for classifying sentences in news articles as subjective or objective across various languages. It has been evaluated in monolingual (Arabic, German, English, Italian, Bulgarian), multilingual, and zero-shot transfer settings (Greek, Polish, Romanian, Ukrainian). It is particularly useful for applications requiring fine-grained text analysis in news contexts, such as misinformation detection, fact-checking, and journalistic tools.
+
+**Limitations:**
+While designed to handle class imbalance through decision threshold calibration, the model's performance on certain languages or specific class distributions might vary. As highlighted in the original work, initial submission errors revealed the sensitivity to proper calibration. The model's primary focus is on news article text, and its generalization to other domains or highly nuanced subjective expressions might vary.
 
 ## Training and evaluation data
 
-More information needed
+The model was trained and evaluated on datasets provided for the **CLEF 2025 CheckThat! Lab Task 1: Subjectivity Detection in News Articles**. Training and development datasets were provided for Arabic, German, English, Italian, and Bulgarian. For final evaluation, additional unseen languages (e.g., Greek, Romanian, Polish, Ukrainian) were included to assess generalization capabilities. The training process focused on enhancing transformer embeddings with sentiment signals and employing decision threshold calibration to mitigate class imbalance prevalent across languages.
+
+## How to use
+You can use this model directly with the Hugging Face `transformers` library for text classification:
+
+```python
+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': ...}]
+
+```
 
 ## Training procedure
 
@@ -72,4 +113,20 @@ The following hyperparameters were used during training:
 - Transformers 4.49.0
 - Pytorch 2.5.1+cu121
 - Datasets 3.3.1
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0
+
+## Citation
+
+If you find our work helpful or inspiring, please feel free to cite it:
+
+```bibtex
+@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}, 
+}
+```