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Model Description
- Purpose: Classifying emotions from text.
- Model architecture: Distilbert
- Training data: Text examples with labels of corresponding emotions ex: sad, happy, love, etc.
Intended Use
- Intended users: For classifying the sentiment of text from examples provided.
- Use cases: Retail, social media, etc.
Limitations
Hardware
- Training Platform: Intel 4th Generation Xeon Processors on the Intel Developer Cloud
Software Optimizations
- Known Optimizations:
- Intel Extension for PyTorch
- Mixed Precision Training (FP32 and BF16)
- Advanced Matrix Extensions (in Xeon Processor)
Ethical Considerations
- Ethical concerns: Beware of potential erroneous results when applying to usecases that involve humans.
More Information
- [Include any additional information, links, or references.]
