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--- |
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license: mit |
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language: |
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- multilingual |
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library_name: transformers |
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pipeline_tag: text-classification |
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widget: |
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- text: "You wont believe what happened to me today" |
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- text: "You wont believe what happened to me today!" |
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- text: "You wont believe what happened to me today..." |
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- text: "You wont believe what happened to me today <3" |
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- text: "You wont believe what happened to me today :)" |
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- text: "You wont believe what happened to me today :(" |
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--- |
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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. |
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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. |
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See the paper, [LEIA: Linguistic Embeddings for the Identification of Affect](https://doi.org/10.1140/epjds/s13688-023-00427-0) for further details. |
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## Evaluation |
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We evaluated LEIA-multilingual on posts with self-annotated emotion labels identified as non-English using an ensemble of language identification tools. |
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The table below shows the macro-F1 scores aggregated across emotion categories for each language: |
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|Language|Macro-F1| |
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|:---:|:---:| |
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|ar |44.18[43.07,45.29]| |
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|da |65.44[60.96,69.83] | |
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|de |60.47[57.58,63.38] | |
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|es |61.67[60.79,62.55] | |
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|fi |45.1[40.96,49.14] | |
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|fr |65.78[63.19,68.36] | |
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|it |63.37[59.67,67.1] | |
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|pt |57.27[55.15,59.4] | |
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|tl |58.37[55.51,61.23] | |
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|tr |45.42[41.17,49.79]| |
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## Citation |
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Please cite the following paper if you find the model useful for your work: |
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```bibtex |
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@article{aroyehun2023leia, |
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title={LEIA: Linguistic Embeddings for the Identification of Affect}, |
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author={Aroyehun, Segun Taofeek and Malik, Lukas and Metzler, Hannah and Haimerl, Nikolas and Di Natale, Anna and Garcia, David}, |
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journal={EPJ Data Science}, |
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volume={12}, |
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year={2023}, |
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publisher={Springer} |
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} |
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``` |
