zeeshan-hf/mlops-emotion-distilbert-group14

DistilBERT for Emotion Classification (IIT Jodhpur MLOps Group 14)

This model is a fine-tuned version of distilbert-base-uncased trained on the dair-ai/emotion dataset. It was developed as part of an end-to-end MLOps pipeline assignment to classify text into six core emotions: sadness, joy, love, anger, fear, and surprise.

📊 Experimentation Summary

We conducted multiple tracking experiments logged via Weights & Biases to discover the optimal hyperparameters:

Experiment Version	Learning Rate	Epochs	Weight Decay	Eval Accuracy	Status
Experiment 1 (Baseline)	2e-5	3	0.01	90.4%	Deprecated
Experiment 2 (Tuned)	5e-5	4	0.05	93.7%	Production Candidate

🛠️ Training Hyperparameters

The winning model (this checkpoint) was trained with the following configurations:

• Learning Rate: 5e-5
• Train Batch Size: 16
• Eval Batch Size: 16
• Epochs: 4
• Weight Decay: 0.05
• Optimizer: AdamW

📈 Evaluation Results

The model achieved a final validation accuracy of 93.7%, demonstrating robust convergence and strong generalization across all 6 emotional categories.

🧪 Tracked with Weights & Biases

All training runs, real-time gradients, and validation loops were monitored transparently. You can inspect our charts under the project workspace: mlops-emotion-classification.