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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

  • Known limitations: N/A

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.]
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