Improve model card: license, tags, paper & GitHub links, usage, and description

This PR significantly enhances the model card for `ModernBERT-base-subjectivity-english` by:

- Correcting the `license` metadata to `cc-by-4.0` as specified in the official GitHub repository.
- Adding `pipeline_tag: text-classification` for improved discoverability on the Hub.
- Including additional `tags` like `modernbert` and `subjectivity-detection`.
- Adding a direct link to the associated paper on Hugging Face Papers.
- Providing a link to the official GitHub repository.
- Populating the previously placeholder sections ("Model description", "Intended uses & limitations", "Training and evaluation data") with detailed information from the paper abstract and GitHub README.
- Adding a clear "How to use" section with a Python code snippet for inference.
- Removing the automatic generation boilerplate comment.

These updates provide a more comprehensive, accurate, and user-friendly model card.

README.md

@@ -1,25 +1,30 @@
 ---
-library_name: transformers
-license: apache-2.0
 base_model: answerdotai/ModernBERT-base
-tags:
-- generated_from_trainer
+language:
+- en
+library_name: transformers
+license: cc-by-4.0
 metrics:
 - accuracy
 - f1
+pipeline_tag: text-classification
+tags:
+- generated_from_trainer
+- modernbert
+- subjectivity-detection
 model-index:
 - name: ModernBERT-base-subjectivity-english
   results: []
-language:
-- en
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # ModernBERT-base-subjectivity-english
 
-This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-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 [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [CheckThat! Lab Task 1 Subjectivity Detection at CLEF 2025](https://arxiv.org/abs/2507.11764).
+
+The model was presented in the paper [AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles](https://huggingface.co/papers/2507.11764).
+
+The official code repository can be found at: [https://github.com/MatteoFasulo/clef2025-checkthat](https://github.com/MatteoFasulo/clef2025-checkthat)
+
 It achieves the following results on the evaluation set:
 - Loss: 1.0478
 - Macro F1: 0.7034
@@ -32,15 +37,44 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model, `ModernBERT-base-subjectivity-english`, is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) designed for subjectivity detection in news articles. It was developed as part of AI Wizards' participation in the CLEF 2025 CheckThat! Lab Task 1, aiming to classify sentences as subjective or objective. The core innovation of this model lies in enhancing transformer-based embeddings by integrating sentiment scores, derived from an auxiliary model, with sentence representations. This approach has shown to significantly boost performance, particularly the subjective F1 score, and aims to improve upon standard fine-tuning methods. To address prevalent class imbalance across languages, the model also employs decision threshold calibration optimized on the development set.
 
 ## Intended uses & limitations
 
-More information needed
+This model is intended for classifying sentences in news articles as subjective (opinion-laden) or objective. This capability is crucial for applications such as combating misinformation, improving fact-checking pipelines, and supporting journalistic efforts. While this specific model is tailored for English, the broader research explored its effectiveness across monolingual (Arabic, German, Italian, Bulgarian) and zero-shot transfer settings (Greek, Polish, Romanian, Ukrainian). A key strength is its use of decision threshold calibration to mitigate class imbalance. However, users should note that the original submission had an issue with skewed class distribution which was later corrected, indicating the importance of proper data splits and calibration for optimal performance.
 
 ## Training and evaluation data
 
-More information needed
+The `ModernBERT-base-subjectivity-english` model was fine-tuned on the English portion of the CheckThat! Lab Task 1: Subjectivity Detection in News Articles dataset provided for CLEF 2025. The training and development datasets included sentences in English (among other languages like Arabic, German, Italian, and Bulgarian). For final evaluation, the broader project also assessed generalization on unseen languages like Greek, Romanian, Polish, and Ukrainian. The training strategy involved augmenting transformer embeddings with sentiment signals and employing decision threshold calibration to improve performance and handle class imbalance.
+
+## How to use
+
+You can use this model directly with the `transformers` library for text classification:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "MatteoFasulo/ModernBERT-base-subjectivity-english"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example text
+text = "The new policy is an absolute disaster for the economy."
+
+# Tokenize and perform inference
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# Get predicted class (0 for OBJ, 1 for SUBJ as per model config)
+predicted_class_id = logits.argmax().item()
+labels = model.config.id2label # Access the label mapping from model config
+predicted_label = labels[predicted_class_id]
+
+print(f"Text: '{text}'")
+print(f"Predicted label: {predicted_label}")
+```
 
 ## Training procedure