distilbert-imdb-sentiment

DistilBERT IMDb Sentiment Classifier

A fine-tuned DistilBERT model for binary sentiment analysis on movie reviews.

Model Description

This model was fine-tuned from distilbert-base-uncased on 5,000 IMDb movie reviews for 3 epochs. It classifies text as POSITIVE or NEGATIVE sentiment.

Training Data

• Source: IMDb Large Movie Review Dataset (stored in SQLite, queried with pandas)
• Train: 5,000 samples | Validation: 1,000 samples
• Label balance: approximately 50% positive, 50% negative

Evaluation Results

Baseline Comparison

Intended Use

Product review analysis, feedback classification, general English sentiment tasks.

Limitations and Bias

• Trained only on English movie reviews performance on other domains may vary
• May not handle Urdu, Roman Urdu, or code-switched text well
• Sarcasm with no obvious negative words may be misclassified
• Very short texts (under 5 words) have lower confidence scores

How to Use

from transformers import pipeline
classifier = pipeline('text-classification', model='YOUR-USERNAME/distilbert-imdb-sentiment')
result = classifier('This movie was absolutely incredible!')
# Output: [{'label': 'POSITIVE', 'score': 0.997}]