This is a BERTweet-base model that has been further pre-trained with preferential masking of emotion words for 100k steps on about 6.3M Vent posts.

This model is meant to be fine-tuned on labeled data or used as feature extractor for downstream tasks.

## 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", "widget": [{"text": ""}]}
transformers pytorch roberta fill-mask en license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 

This is a BERTweet-base model that has been further pre-trained with preferential masking of emotion words for 100k steps on about 6.3M Vent posts.

This model is meant to be fine-tuned on labeled data or used as feature extractor for downstream tasks.

Please cite the following paper if you find the model useful for your work:

TAGS
#transformers #pytorch #roberta #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #roberta #fill-mask #en #license-mit #autotrain_compatible #endpoints_compatible #region-us 
