rrpetroff
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huggingface-workshop-emotions-bert

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+license: apache-2.0
+language:
+- en
 ---
+# Model Card for Simple DistilBERT Emotions Workshop Model
+## Model Description
+- **Purpose**: This model is fine-tuned to perform multi-class emotion classification. It can identify various emotions in text, such as joy, sadness, love, anger, fear, and surprise.
+- **Model architecture**: The model is based on the `distilbert-base-uncased` architecture, a distilled version of the BERT model which is smaller and faster but retains most of its predictive power.
+- **Training data**: The model was trained on the `emotion` dataset from Hugging Face's datasets library. This dataset includes text labeled with different emotions. During preprocessing, texts were tokenized, and padding and truncation were applied to standardize their lengths.
+## Intended Use
+- **Intended users**: This model is intended for developers and researchers interested in emotion analysis in text, including applications in social media sentiment analysis, customer feedback interpretation, and mental health assessment.
+- **Use cases**: Potential use cases include analyzing social media posts for emotional content, enhancing chatbots to understand user emotions, and helping mental health professionals in identifying emotional states from text-based communications.
+## Limitations
+- **Known limitations**: The model's accuracy may vary depending on the context and the dataset's representativeness. It may not perform equally well on texts from domains significantly different from the training data.
+## Hardware
+- **Training Platform**: The model was trained on 4th Generation Intel Xeon Processors available on the Intel Developer Cloud (cloud.intel.com). The training completed in under 8 minutes, demonstrating the efficiency of Intel hardware optimizations.
+## Ethical Considerations
+- **Ethical concerns**: Care should be taken to ensure that the model is not used in sensitive applications without proper ethical considerations, especially in scenarios that could impact individual privacy or mental health.
+## More Information
+- **Training Setup**: The training leveraged Intel extensions for PyTorch (IPEX) to optimize training efficiency on Intel hardware. Mixed precision training (FP32 and BF16) was enabled, contributing to the rapid training time.