|
--- |
|
license: mit |
|
language: |
|
- multilingual |
|
library_name: transformers |
|
pipeline_tag: text-classification |
|
widget: |
|
- text: "You wont believe what happened to me today" |
|
- text: "You wont believe what happened to me today!" |
|
- text: "You wont believe what happened to me today..." |
|
- text: "You wont believe what happened to me today <3" |
|
- text: "You wont believe what happened to me today :)" |
|
- text: "You wont believe what happened to me today :(" |
|
--- |
|
This is an emotion classification model based on fine-tuning of a Bernice model (which is a multilingual pre-trained model trained on multilingual Twitter data) on self-labeled emotion dataset (Lykousas et al., 2019) in English that corresponds to Anger, Fear, Sadness, Joy, and Affection. |
|
See the paper, [LEIA: Linguistic Embeddings for the Identification of Affect](https://arxiv.org/abs/2304.10973) for further details. |
|
|
|
## Evaluation |
|
We evaluated LEIA-multilingual on Vent posts with self-annotated emotion labels identified as non-English using an ensemble of language identefication tools. |
|
The table below shows the macro-F1 scores across emotion categories and languages: |
|
|
|
|
|
|
|
|Language|Macro-F1| |
|
|:---:|:---:| |
|
|ar |44.18[43.07,45.29]| |
|
|da |65.44[60.96,69.83] | |
|
|de |60.47[57.58,63.38] | |
|
|es |61.67[60.79,62.55] | |
|
|fi |45.1[40.96,49.14] | |
|
|fr |65.78[63.19,68.36] | |
|
|it |63.37[59.67,67.1] | |
|
|pt |57.27[55.15,59.4] | |
|
|tl |58.37[55.51,61.23] | |
|
|tr |45.42[41.17,49.79]| |
|
|
|
|
|
|
|
## Citation |
|
Please cite the following paper if you find the model useful for your work: |
|
```bibtex |
|
@article{aroyehun2023leia, |
|
title={LEIA: Linguistic Embeddings for the Identification of Affect}, |
|
author={Aroyehun, Segun Taofeek and Malik, Lukas and Metzler, Hannah and Haimerl, Nikolas and Di Natale, Anna and Garcia, David}, |
|
journal={EPJ Data Science}, |
|
volume={12}, |
|
year={2023}, |
|
publisher={Springer} |
|
} |
|
``` |
