---
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 pre-trained model trained on multilingual Twitter data.
The fine-tuning dataset is a subset of the 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://doi.org/10.1140/epjds/s13688-023-00427-0) for further details. 

## Evaluation
We evaluated LEIA-multilingual on posts with self-annotated emotion labels identified as non-English using an ensemble of language identification tools. 
The table below shows the macro-F1 scores aggregated across emotion categories for each language:


  
|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}
}
```