j-hartmann/emotion-english-distilroberta-base
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:
- anger 🤬
- disgust 🤢
- fear
- joy
- neutral
- sadness
- 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 | – |
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