File size: 2,255 Bytes
d749f81 1bbf0c3 d749f81 1bbf0c3 d749f81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
---
language:
- de
---
# Scene Segmenter for the Shared Task on Scene Segmentation
This is the scene segmenter model that is being used in [LLpro](https://github.com/cophi-wue/LLpro). On borders between sentences, it predicts one of the following labels:
- `B-Scene`: the preceding sentence began a new *Scene*.
- `B-Nonscene`: the preceding sentence began a new *Non-Scene*.
- `Scene`: the preceding sentence belongs to a *Scene*, but does not begin a new one – i.e., the scene continues.
- `Nonscene`: the preceding sentence belongs to a *Noncene*, but does not begin a new one – i.e., the non-scene continues.
Broadly speaking, the model is being used in a token classification setup. A sequence of multiple sentences is represented by interspersing the respective tokenizations with the special `[SEP]` token.
On these `[SEP]` tokens, the linear classification layer predicts one of the four above classes.
The model is trained on the dataset corresponding to the [KONVENS 2021 Shared Task on Scene Segmentation](http://lsx-events.informatik.uni-wuerzburg.de/stss-2021/task.html) [(Zehe et al., 2021)][http://ceur-ws.org/Vol-3001/#paper1] fine-tuning the domain-adapted [lkonle/fiction-gbert-large](https://huggingface.co/lkonle/fiction-gbert-large). ([Training code](https://github.com/cophi-wue/LLpro/blob/main/contrib/train_scene_segmenter.py))
F1-Score:
- **40.22** on Track 1 (in-domain dime novels)
- **35.09** on Track 2 (out-of-domain high brow novels)
The respective test datasets are only available to the task organizers; the task organizers evaluated this model on their private test set and report above scores. See the [KONVENS paper](http://ceur-ws.org/Vol-3001/#paper1) for a description of their metric.
---
**Demo Usage**:
```python
TODO
```
**Cite**:
Please cite the following paper when using this model.
```
@inproceedings{ehrmanntraut-et-al-llpro-2023,
location = {Ingolstadt, Germany},
title = {{LLpro}: A Literary Language Processing Pipeline for {German} Narrative Text},
booktitle = {Proceedings of the 10th Conference on Natural Language Processing ({KONVENS} 2022)},
publisher = {{KONVENS} 2023 Organizers},
author = {Ehrmanntraut, Anton and Konle, Leonard and Jannidis, Fotis},
date = {2023},
}
``` |