emotion-classification-distilbert

Emotion Classification with DistilBERT

Fine-tuned DistilBERT model for classifying text into 6 emotions: sadness, joy, love, anger, fear, and surprise.

Model Details

• Developed by: Mahnoor Iqbal
• Model type: Text Classification
• Language: English
• License: MIT
• Finetuned from: distilbert/distilbert-base-uncased

Uses

Direct Use

Classifying short English text into one of 6 emotion categories. Suitable for social media analysis, sentiment pipelines, or mental health monitoring tools.

Out-of-Scope Use

• Non-English text
• Long documents (truncated at 128 tokens)
• Detecting mixed or blended emotions (returns single label only)

How to Get Started

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="MahnoorIqbal/emotion-classification-distilbert"
)

classifier("I can't believe this actually worked!")
# [{'label': 'surprise', 'score': 0.91}]

Training Details

Training Data

praveengovi/emotions-dataset-for-nlp — 16,000 training samples across 6 emotion classes.

Class distribution is imbalanced (joy: 5,362 vs surprise: 572), handled via inverse-frequency class weights in the loss function.

Training Hyperparameters

• Base model: distilbert-base-uncased
• Epochs: 2
• Batch size: 32
• Learning rate: 2e-5
• Weight decay: 0.01
• Max token length: 128
• Precision: fp16
• Class weighting: Yes (inverse-frequency)

Evaluation

Results on Test Set (2000 samples)

• Accuracy: 0.92
• Macro F1: 0.88
• Weighted F1: 0.92

Limitations

• Surprise performs weakest (F1: 0.73) due to limited training samples (66 in test set)
• Joy and love are occasionally confused due to semantic overlap
• Fear and surprise are occasionally confused due to semantic overlap