File size: 1,977 Bytes
22fcba3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | # 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**
---
## 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
}
]
```
---
## 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.
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