| | --- |
| | 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} |
| | } |
| | ``` |
