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# Argument–Keypoint Matching with DistilBERT
This model predicts whether an argument is correctly matched (**Apparié**) or not (**Non-Apparié**) with a given key point.
## Model Description
- **Base Model:** DistilBERT (uncased)
- **Task:** Binary text-pair classification
- **Training Data:** [IBM ArgKP-2023 dataset (~32,000 examples)](https://research.ibm.com/haifa/dept/vst/debating_data.shtml)
- **Labels:**
- `0` — Non-Apparié
- `1` — Apparié
- **Input:** (argument, key_point)
- **Output:** Predicted class + probabilities
## Performance
- Strong accuracy and F1 score on evaluation data
- Reliable predictions across both labels
## Training
Trained on a balanced argument–keypoint dataset
Exported using save_pretrained
## Citation
```
@misc{argument-keypoint-matching,
author = {Malek Messaoudi},
title = {Argument–Keypoint Matching with DistilBERT},
year = {2025},
publisher = {Hugging Face},
howpublished = {{\\url{{https://huggingface.co/NLP-Debater-Project/destlibert-keypoint-matching}}}}
}
````
## License
MIT License |