emotion-english-distilroberta-base EU AI Act Compliance Profile

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.

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