j-hartmann

--- language: "en" tags: - distilroberta - sentiment - emotion - twitter - reddit

This RoBERTa-based model can classify the sentiment of English language text in 3 classes: The model was fine-tuned on 5,304 manually annotated social media posts. The hold-out accuracy is 86.1%. For details on the training approach see Web Appendix F in Hartmann et al. (2021). Reference Please cite this paper when you use our model. Feel free to reach out to [email protected] with any questions or feedback you may have.

With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 🤬 2) disgust 🤢 3) fear 😨 4) joy 😀 5) neutral 😐 6) sadness 😭 7) surprise 😲 The model is a fine-tuned checkpoint of RoBERTa-large. For further details on this emotion model, please refer to the model card of its DistilRoBERTa version.