| --- |
| license: mit |
| language: |
| - en |
| 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 further pre-training of BERTweet-base with preferential masking of emotion words and fine-tuning on a subset of a self-labeled emotion dataset (Lykousas et al., 2019) that corresponds to Anger, Fear, Sadness, Joy, and Affection. The paper, [LEIA: Linguistic Embeddings for the Identification of Affect](https://doi.org/10.1140/epjds/s13688-023-00427-0) provides further details on the model and its evauation. |
|
|
| See [LEIA-large](https://huggingface.co/LEIA/LEIA-large) for a similar model based on BERTweet-large. |
| ## 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} |
| } |
| ``` |
