| --- |
| license: cc-by-4.0 |
| language: en |
| base_model: roberta-base |
| pipeline_tag: text-classification |
| tags: [literary-studies, embodiment, narrative, digital-humanities] |
| --- |
| |
| # Embodiment Classifier (English narrative fiction) |
|
|
| Sentence-level classifier assigning one of 11 embodiment categories to sentences |
| from English-language prose fiction. Fine-tuned from `roberta-base`. |
|
|
| Labels: `no_rel`, `character_move`, `haptic_material`, `character_feltness`, |
| `facial_expr`, `sight_human`, `book_paratext`, `haptic_human_animal`, |
| `sight_material`, `segmentation_error`, `gesture`. |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import pipeline |
| clf = pipeline("text-classification", model="katrohrb/embodiment-classifier") |
| clf("She reached out and touched the rough bark.") |
| # -> [{'label': 'haptic_material', ...}] |
| ``` |
|
|
|
|
| ## Performance |
|
|
| Held-out test set (n=580): micro-F1 **0.805** [0.772, 0.835], macro-F1 0.797. |
| 5-fold CV: 0.808 +/- 0.022. Human inter-annotator agreement ceiling: Cohen's kappa 0.760. |
|
|
| ## Data and code |
|
|
| Codebook, gold annotations, and reproduction notebooks: |
| https://github.com/katrin-rohrbacher/embodiment-paper |
|
|
| ## Citation |
|
|
| <Add the bibliographic reference once the paper is out.> |
|
|
