Improve model card: Add pipeline tag, update license, expand content and add links

README.md

@@ -1,18 +1,22 @@
 ---
+base_model:
+- microsoft/mdeberta-v3-base
+language:
+- bg
 library_name: transformers
-tags:
-- generated_from_trainer
+license: cc-by-4.0
 metrics:
 - accuracy
 - f1
+tags:
+- generated_from_trainer
+- text-classification
+pipeline_tag: text-classification
 model-index:
 - name: mdeberta-v3-base-subjectivity-sentiment-bulgarian
   results: []
-license: mit
-language:
-- bg
-base_model:
-- microsoft/mdeberta-v3-base
+datasets:
+- MatteoFasulo/clef2025_checkthat_task1_subjectivity
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -20,7 +24,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # mdeberta-v3-base-subjectivity-sentiment-bulgarian
 
-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) on the [CheckThat! Lab Task 1 Subjectivity Detection at CLEF 2025](https://arxiv.org/abs/2507.11764).
 It achieves the following results on the evaluation set:
 - Loss: 0.4555
 - Macro F1: 0.8291
@@ -33,15 +37,50 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model is a fine-tuned `mDeBERTaV3-base` classifier designed for **subjectivity detection in news articles**, specifically for the Bulgarian language. It classifies sentences as either **subjective** (opinion-laden) or **objective**.
+
+The core innovation of this approach, as detailed in the associated paper, is the enhancement of transformer-based embeddings by integrating sentiment scores derived from an auxiliary model. This sentiment-augmented architecture aims to improve upon standard fine-tuning, demonstrating significant performance boosts, particularly in subjective F1 score. The training also incorporates robust decision threshold calibration to effectively address class imbalance.
+
+This model is part of the AI Wizards' participation in the CLEF 2025 CheckThat! Lab Task 1: Subjectivity Detection in News Articles. The broader research explored its application across monolingual, multilingual, and zero-shot settings.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for classifying sentences as subjective or objective within news articles, which is a key component in **combating misinformation**, **improving fact-checking pipelines**, and **supporting journalists**. This specific checkpoint is optimized for the **Bulgarian language**.
+
+**Limitations:**
+*   While the sentiment-augmented approach showed consistent performance gains, the paper notes that BERT-like models still surpassed LLM baselines in various scenarios, indicating areas for further research.
+*   The model's effectiveness may vary for domains or linguistic styles significantly different from the news articles it was trained on.
+*   Generalization to other languages beyond those explicitly evaluated (Arabic, German, English, Italian, Bulgarian, Greek, Romanian, Polish, Ukrainian) is not guaranteed without further fine-tuning.
 
 ## Training and evaluation data
 
-More information needed
+This model was fine-tuned on the data provided for the **CLEF 2025 CheckThat! Lab Task 1: Subjectivity Detection in News Articles**. Training and development datasets were provided for various languages, including Bulgarian. The final evaluation included additional unseen languages to assess generalization capabilities.
+
+To address class imbalance, a common issue across languages, decision threshold calibration was employed during training and optimized on the development set. The base model, `microsoft/mdeberta-v3-base`, was pretrained on a diverse multilingual corpus.
+
+## How to use
+
+You can use this model directly with the Hugging Face `transformers` library:
+
+```python
+from transformers import pipeline
+
+# Load the text classification pipeline
+classifier = pipeline(
+    "text-classification",
+    model="MatteoFasulo/mdeberta-v3-base-subjectivity-bulgarian",
+    tokenizer="microsoft/mdeberta-v3-base",
+)
+
+# Example usage:
+result1 = classifier("По принцип никой не иска войни, но за нещастие те се случват.")
+print(f"Classification: {result1}")
+# Expected output: [{'label': 'SUBJ', 'score': ...}]
+
+result2 = classifier("В един момент започнал сам да търси изход за своето спасение и здраве")
+print(f"Classification: {result2}")
+# Expected output: [{'label': 'OBJ', 'score': ...}]
+```
 
 ## Training procedure
 
@@ -73,4 +112,30 @@ 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
+
+## 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)
+
+## Project Page
+
+Explore the collection of models and interactive results on the Hugging Face Hub:
+[AI Wizards @ CLEF 2025 - CheckThat! Lab - Task 1 Subjectivity](https://huggingface.co/collections/MatteoFasulo/clef-2025-checkthat-lab-task-1-subjectivity-6878f0199d302acdfe2ceddb)
+
+## 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}, 
+}
+```