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--- |
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language: "en" |
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tags: |
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- sentiment |
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- emotion |
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- twitter |
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widget: |
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- text: "Oh wow. I didn't know that." |
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- text: "This movie always makes me cry.." |
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--- |
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## Description |
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With this model, you can classify emotions in English text data. The model was trained on diverse datasets and predicts 7 emotions: |
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1) anger |
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2) disgust |
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3) fear |
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4) joy |
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5) neutral |
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6) sadness |
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7) surprise |
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The model is a fine-tuned checkpoint of DistilRoBERTa-base. |
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## Application |
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a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab: |
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[](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/simple_emotion_pipeline.ipynb) |
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b) Run emotion model on multiple examples and full datasets (e.g., .csv files) on Google Colab: |
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[](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/emotion_prediction_example.ipynb) |
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## Contact |
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Please reach out to jochen.hartmann@uni-hamburg.de if you have any questions or feedback. |
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Thanks to Samuel Domdey and chrsiebert for their support in making this model available. |
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## Appendix |
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Please find an overview of the datasets used for training below: |
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|Name|anger|disgust|fear|joy|neutral|sadness|surprise| |
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|---|---|---|---|---|---|---|---| |
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|Crowdflower (2016)|Yes|No|No|Yes|Yes|Yes|Yes| |
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|Elvis et al. (2018)|Yes|Yes|Yes|Yes|No|Yes|Yes| |
