---
title: IMDB Sentiment Classifier
emoji: 🎬
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit
---

# 🎬 IMDB Sentiment Classifier

A binary sentiment classifier fine-tuned on the IMDB movie reviews dataset using LoRA (Low-Rank Adaptation) with DistilBERT (≤ 110M parameters).

## 🔍 About the Model

- **Model Type:** Binary Sentiment Classification
- **Base Model:** `distilbert-base-uncased`
- **Training Dataset:** IMDB Dataset (50K movie reviews)
- **Task:** Classify text as Positive or Negative sentiment
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **Parameters:** ~66M total, ~890K trainable with LoRA

## 🚀 Features

- **Single Review Analysis:** Enter any movie review text
- **Batch Processing:** Analyze multiple reviews at once
- **File Upload:** Upload `.txt` or `.csv` files for analysis
- **Confidence Scores:** See how confident the model is
- **Visual Feedback:** Color-coded results with emojis

## 📊 Example Results

| Review | Sentiment | Confidence |
|--------|-----------|------------|
| "This movie was absolutely fantastic!" | Positive 😊 | 98% |
| "Terrible film, waste of time." | Negative 😞 | 95% |
| "Average movie, nothing special." | Negative 😞 | 72% |

## 🛠️ Technical Details

### Model Architecture
```python
Model: DistilBERT + LoRA
- Base: distilbert-base-uncased
- LoRA Rank: 8
- Trainable Parameters: ~890K (1.35% of total)
- Max Sequence Length: 256 tokens