
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## 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.]
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["intel", "ipex", "bf16"], "metrics": ["accuracy"]}
transformers safetensors distilbert text-classification intel ipex bf16 en license:mit autotrain_compatible endpoints_compatible region:us