Emotion English DistilRoBERTa-base

Description ℹ

With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) 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 DistilRoBERTa-base. For a ‘non-distilled’ emotion model, please refer to the model card of the RoBERTa-large version.

Application 🚀

a) Run emotion model with 3 lines of code on single text example using Hugging Face’s pipeline command on Google Colab:

from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
classifier("I love this!")


Output:
[[{'label': 'anger', 'score': 0.004419783595949411},
{'label': 'disgust', 'score': 0.0016119900392368436},
{'label': 'fear', 'score': 0.0004138521908316761},
{'label': 'joy', 'score': 0.9771687984466553},
{'label': 'neutral', 'score': 0.005764586851000786},
{'label': 'sadness', 'score': 0.002092392183840275},
{'label': 'surprise', 'score': 0.008528684265911579}]]


b) Run emotion model on multiple examples and full datasets (e.g., .csv files) on Google Colab:

Contact 💻

Please reach out to jochen.hartmann@tum.de if you have any questions or feedback.
Thanks to Samuel Domdey and chrsiebert for their support in making this model available.

Reference ✅

For attribution, please cite the following reference if you use this model. A working paper will be available soon.
Jochen Hartmann, "Emotion English DistilRoBERTa-base". https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/, 2022.


BibTex citation:
@misc{hartmann2022emotionenglish,
author={Hartmann, Jochen},
title={Emotion English DistilRoBERTa-base},
year={2022},
howpublished = {\url{https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/}},
}


Appendix 📚

Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets. The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here.

Name	anger	disgust	fear	joy	neutral	sadness	surprise
Crowdflower (2016)	Yes	–	–	Yes	Yes	Yes	Yes
Emotion Dataset, Elvis et al. (2018)	Yes	–	Yes	Yes	–	Yes	Yes
GoEmotions, Demszky et al. (2020)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
ISEAR, Vikash (2018)	Yes	Yes	Yes	Yes	–	Yes	–
MELD, Poria et al. (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
SemEval-2018, EI-reg, Mohammad et al. (2018)	Yes	–	Yes	Yes	–	Yes	–