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---
license: mit
language:
- en
metrics:
- accuracy
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
tags:
- sentiment-analysis
- bert
- imdb
- fine-tuned
- text-classification
---
# 📊 Sentiment Analysis with Fine-Tuned BERT (IMDB)
This repository contains a fine-tuned BERT model for binary sentiment classification using the IMDB movie reviews dataset. The model classifies reviews as **positive** or **negative**, and is built using Hugging Face Transformers and PyTorch.
## 🚀 Model Performance
| Metric | Value |
|------------------|-------------|
| Accuracy | 89.4% |
| Validation Loss | 0.375 |
| Epochs Trained | 3 |
| Inference Speed | ~434 samples/sec |
## 🧠 Model Details
- **Base Model**: `bert-base-uncased`
- **Dataset**: IMDB (binary sentiment)
- **Framework**: Hugging Face Transformers
- **Fine-Tuning Setup**:
- Learning rate: 2e-5
- Batch size: 32
- Mixed-precision: ✅ (`fp16`)
- Early stopping: ❌ (trained for full 3 epochs)
## 🛠️ How to Use
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="Harsha901/tinybert-imdb-sentiment-analysis-model")
classifier("This movie was absolutely amazing!")
|