| # 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 |