# BERT Fine-Tuning for IMDb Sentiment Classification

A fine-tuned **BERT Base Uncased** model for **binary sentiment classification** on the IMDb Movie Reviews dataset.

This project demonstrates the complete fine-tuning workflow using the Hugging Face ecosystem, from dataset preprocessing and tokenization to model training, evaluation, inference, and deployment.

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## Model Details

* **Base Model:** `bert-base-uncased`
* **Task:** Binary Sentiment Classification
* **Dataset:** IMDb Movie Reviews
* **Framework:** Hugging Face Transformers
* **Training Framework:** Trainer API
* **Language:** English

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## Training Pipeline

The model was trained using the following workflow:

* Dataset loading using Hugging Face Datasets
* Tokenization with `AutoTokenizer`
* Fine-tuning using `AutoModelForSequenceClassification`
* Evaluation with Accuracy metric
* Mixed precision (FP16) training when CUDA is available
* Model exported using SafeTensors

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## Performance

The fine-tuned model learns to classify movie reviews into:

* **LABEL_0 → Negative**
* **LABEL_1 → Positive**

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## Usage

```python
from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="YOUR_USERNAME/BERT-Fine-Tuning"
)

classifier("This movie was absolutely amazing!")
```

Example output:

```python
[
    {
        "label": "LABEL_1",
        "score": 0.998
    }
]
```

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## Repository Contents

* Fine-tuned model weights
* Tokenizer files
* Configuration files
* SafeTensors checkpoint

The complete training notebook, source code, and documentation are available in the accompanying GitHub repository.

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## Future Improvements

* LoRA / PEFT fine-tuning
* Multi-class sentiment classification
* Hyperparameter optimization
* Model quantization
* ONNX and TensorRT deployment
* Production inference benchmarking

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## License

This project is released for educational and research purposes.

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Built with ❤️ by the author.