This is an emotion classification model based on further pre-training of BERTweet-basee with preferential maskig 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://arxiv.org/abs/2304.10973) 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={arXiv preprint arXiv:2304.10973},
  year={2023}
}
```
{"language": ["en"], "license": "mit", "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 :("}]}
transformers pytorch roberta text-classification en arxiv:2304.10973 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-2304.10973 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

This is an emotion classification model based on further pre-training of BERTweet-basee with preferential maskig 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 provides further details on the model and its evauation. 

See LEIA-large for a similar model based on BERTweet-large.
Please cite the following paper if you find the model useful for your work:

TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-2304.10973 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #roberta #text-classification #en #arxiv-2304.10973 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
