| --- |
| license: mit |
| language: |
| - en |
| base_model: |
| - answerdotai/ModernBERT-base |
| --- |
| ## 🔍 ModernBERT-CritiQ: Critical Question Usefulness Classifier |
|
|
| This model classifies **critical questions** as either `Useful` or `Non-Useful` in the context of an argument (intervention). It is fine-tuned on Critical Question Generation datasets using a ModernBERT architecture with extended context. |
|
|
| --- |
|
|
| ## 💡 How to Use |
|
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| ### 🛠 Install Dependencies |
|
|
| ```bash |
| pip install transformers |
| ``` |
|
|
| ### 🚀 Run with HuggingFace `pipeline` |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline("text-classification", model="MidhunKanadan/ModernBERT-CritiQ") |
| |
| intervention = "Investing in public transport reduces carbon emissions and benefits everyone." |
| question = "What about the costs of implementing this system?" |
| |
| text = f"Intervention: {intervention} [SEP] Critical Question: {question}" |
| result = classifier(text)[0] |
| |
| print(f"Label: {result['label']}, Confidence: {result['score']:.4f}") |
| ``` |
|
|
| --- |
|
|
| ## 🧠 Expected Output |
|
|
| ``` |
| Label: Useful, Confidence: 0.9539 |
| ``` |
