README.md

---
language: en
tags:
- emotion-classification
- text-classification
- distilbert
datasets:
- dair-ai/emotion
metrics:
- accuracy
---

# Emotion Classification Model

## Model Description
This model fine-tunes **DistilBERT** for the task of **emotion classification**. It is trained to classify text into one of six emotions: **sadness, joy, love, anger, fear, and surprise**. The model is designed for natural language processing applications where understanding emotions in text is valuable, such as social media analysis, customer feedback, and mental health monitoring.

## Training and Evaluation
- **Training Dataset:** [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) (16,000 examples)
- **Validation Accuracy:** 94.5%
- **Test Accuracy:** 93.1%
- **Training Time:** 169.2 seconds (~2 minutes 49 seconds)
- **Hyperparameters:**
  - Learning Rate: 5e-5
  - Batch Size (Train): 32
  - Batch Size (Validation): 64
  - Epochs: 3
  - Weight Decay: 0.01
  - Optimizer: AdamW
  - Evaluation Strategy: Epoch-based

## Usage
```python
from transformers import pipeline

# Load the model from HuggingFace Hub
classifier = pipeline("text-classification", model="your-username/emotion-classification-model")

# Example usage
text = "I’m so happy today!"
result = classifier(text)
print(result)


## Limitations
**Biases in Dataset**
  The model was trained on the dair-ai/emotion dataset, which may not represent the full diversity of language use across demographics, regions, or cultures.
As a result, it might underperform on texts containing:

  - Slang or Informal Language
  For example, "I'm shook!" may not be accurately classified as an expression of surprise.

  - Non-Standard Grammar or Dialects
  Variants like African American Vernacular English (AAVE) or regional dialects might lead to misclassifications.

  - Limited Contextual Understanding
  The model processes inputs as isolated pieces of text, without awareness of surrounding context.
  For instance:
    - Sarcasm
    "Oh great, another rainy day!" may not be correctly classified as expressing frustration.

    - Complex or Mixed Emotions
    Texts expressing multiple emotions (e.g., "I’m angry but also relieved") may be oversimplified into a single label.

    - Short Texts and Ambiguity
    Performance can degrade for very short texts (e.g., one or two words) due to insufficient context.
      For example:
      - "Wow!" might be classified as joy or surprise depending on subtle cues not present in such brief inputs.
      - Ambiguous inputs like "Okay" or "Fine" are challenging without additional context.

  - Domain-Specific Language
  The model may underperform on text from specialized domains (e.g., legal, medical, or technical writing) or content involving code-mixed or multilingual inputs.
  For example, "Estoy feliz!" might not be recognized as expressing joy without multilingual training.


## Potential Improvements
- Data Augmentation
Including additional datasets or generating synthetic data can improve generalization.

- Longer Training
Training for more epochs could marginally increase accuracy, although diminishing returns are likely.

- Larger Models
Fine-tuning larger models like BERT or RoBERTa may yield better results for nuanced understanding.

- Bias Mitigation
Incorporating fairness-aware training methods or balanced datasets could reduce biases.