metadata

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 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.

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.

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 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.

Model Name on Hugging Face: eduardo-alvarez/distilbert-emotions-workshop
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.