distilbert-base-uncased-go-emotions-student
Maintainer: joeddav
64
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
The distilbert-base-uncased-go-emotions-student model is a distilled version of a zero-shot classification pipeline trained on the unlabeled GoEmotions dataset. The maintainer explains that this model was trained with mixed precision for 10 epochs using a script for distilling an NLI-based zero-shot model into a more efficient student model. While the original GoEmotions dataset allows for multi-label classification, the teacher model used single-label classification to create pseudo-labels for the student.
Similar models include distilbert-base-multilingual-cased-sentiments-student, which was distilled from a zero-shot classification pipeline on the Multilingual Sentiment dataset, and roberta-base-go_emotions, a model trained directly on the GoEmotions dataset.
Model Inputs and Outputs
Inputs
- Text: The model takes text input, such as a sentence or short paragraph.
Outputs
- Emotion Labels: The model outputs a list of predicted emotion labels and their corresponding scores. The model predicts the probability of the input text expressing emotions like anger, disgust, fear, joy, sadness, and surprise.
Capabilities
The distilbert-base-uncased-go-emotions-student model can be used for zero-shot emotion classification on text data. While it may not perform as well as a fully supervised model, it can provide a quick and efficient way to gauge the emotional tone of text without the need for labeled training data.
What Can I Use It For?
This model could be useful for a variety of text-based applications, such as:
- Analyzing customer feedback or social media posts to understand the emotional sentiment expressed
- Categorizing movie or book reviews based on the emotions they convey
- Monitoring online discussions or forums for signs of emotional distress or conflict
Things to Try
One interesting aspect of this model is that it was distilled from a zero-shot classification pipeline. This means the model was trained without any labeled data, relying instead on pseudo-labels generated by a teacher model. It would be interesting to experiment with different approaches to distillation or to explore how the performance of this student model compares to a fully supervised model trained directly on the GoEmotions dataset.
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