distilbert-base-uncased-go-emotions-student
| Property | Value |
|---|---|
| License | MIT |
| Framework Support | PyTorch, TensorFlow |
| Primary Task | Emotion Classification |
| Language | English |
What is distilbert-base-uncased-go-emotions-student?
This is a specialized emotion classification model that leverages knowledge distillation techniques to create an efficient student model from a zero-shot classification pipeline. The model was trained on the GoEmotions dataset using an innovative approach that requires only unlabeled data, making it particularly interesting for scenarios where labeled data is scarce.
Implementation Details
The model was trained using a mixed precision approach over 10 epochs, implementing knowledge distillation from a more complex teacher model. It's built on the DistilBERT architecture, which is already an optimized version of BERT, making this implementation particularly efficient for deployment.
- Built on DistilBERT base uncased architecture
- Trained with mixed precision for optimization
- Utilizes knowledge distillation from zero-shot classification
- Processes single-label classification despite GoEmotions' multi-label nature
Core Capabilities
- Emotion classification from text input
- Efficient inference with reduced model complexity
- Support for both PyTorch and TensorFlow frameworks
- Specialized for English language text processing
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its training approach, using zero-shot distillation to create an efficient emotion classifier without requiring labeled training data. This makes it a valuable example of how complex NLI-based zero-shot models can be compressed into more efficient implementations.
Q: What are the recommended use cases?
The model is best suited for research and experimental applications in emotion classification, particularly when computational efficiency is important. However, it's important to note that it may not perform as well as fully supervised models, making it more suitable for proof-of-concept or resource-constrained scenarios.
