DistilBERT Fine-tuned on Amazon Reviews (5-Star Rating)
Model Description
This model is a fine-tuned version of distilbert-base-uncased for 5-class sentiment classification, predicting star ratings (1-5) from Amazon product reviews.
Training Data
- Dataset: SetFit/amazon_reviews_multi_en
- Train samples: 20,000 (subset)
- Test samples: 2,000 (subset)
- Classes: 1 star, 2 stars, 3 stars, 4 stars, 5 stars
Training Procedure
- Base model: distilbert-base-uncased
- Epochs: 3
- Batch size: 16
- Learning rate: 2e-5
- Max sequence length: 256
Evaluation Results
- Accuracy: 54.95%
- Off-by-one accuracy: 92.45%
Note: 55% accuracy on a 5-class problem is 2.75x better than random chance (20%). The high off-by-one accuracy (92%) indicates the model rarely makes catastrophic errors.
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="Nav772/distilbert-amazon-reviews-5star")
result = classifier("This product exceeded my expectations! Great quality.")
print(result)
Limitations
- Trained on Amazon product reviews; may not generalize to other review domains
- Adjacent star ratings (e.g., 2 vs 3 stars) are inherently difficult to distinguish due to subjective labeling
- English language only
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