asadullahrehmann/imdb-distilbert-sentimentops

IMDB Sentiment Analysis — DistilBERT

Fine-tuned DistilBERT model for binary sentiment classification on IMDB movie reviews.

Model Description

This model is a fine-tuned version of distilbert-base-uncased on the IMDB dataset. It classifies movie reviews as positive or negative.

Built as part of the SentimentOps MLOps project — an end-to-end pipeline covering data processing, model training, experiment tracking with MLflow, REST API with FastAPI, CLI tooling, and Docker deployment.

Training

• Base model: distilbert-base-uncased
• Dataset: IMDB (25,000 train / 25,000 test)
• Epochs: 3
• Batch size: 16
• Learning rate: 2e-5
• Hardware: NVIDIA Tesla T4 (Google Colab)
• Training time: ~50 minutes

Performance

Metric	Value
Test Accuracy	97.61%
vs TF-IDF baseline	+7%

Usage

from transformers import pipeline

classifier = pipeline(
    "sentiment-analysis",
    model="YOUR_USERNAME/imdb-distilbert-sentimentops"
)

result = classifier("This movie was absolutely fantastic!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9976}]

Comparison with Baseline

Model	Accuracy	Inference
TF-IDF + LogReg	90.0%	~1ms
DistilBERT (this)	97.6%	~50ms

Project

Part of SentimentOps — a full MLOps pipeline:

• GitHub: https://github.com/as4d-4k/sentimentops
• Portfolio: https://portfolio-steel-nine-aur.vercel.app