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--- |
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language: en |
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tags: |
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- emotion-classification |
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datasets: |
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- go-emotions |
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- bdotloh/empathetic-dialogues-contexts |
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--- |
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# Model Description |
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Yet another Transformer model fine-tuned for approximating another non-linear mapping between X and Y? That's right! |
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This is your good ol' emotion classifier - given an input text, the model outputs a probability distribution over a set of pre-selected emotion words. In this case, it is 32, which is the number of emotion classes in the [Empathetic Dialogues](https://huggingface.co/datasets/bdotloh/empathetic-dialogues-contexts) dataset. |
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This model is built "on top of" a [distilbert-base-uncased model fine-tuned on the go-emotions dataset](https://huggingface.co/bhadresh-savani/bert-base-go-emotion). Y'all should really check out that model, it even contains a jupyter notebook file that illustrates how the model was trained (bhadresh-savani if you see this, thank you!). |
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## Training data |
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## Training procedure |
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### Preprocessing |
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## Evaluation results |
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### Limitations and bias |
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Well where should we begin... |
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EmpatheticDialogues: |
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1) Unable to ascertain the degree of cultural specificity for the context that a respondent described when given an emotion label (i.e., p(description | emotion, *culture*)) |
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2) ... |
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