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license: mit language: en datasets:

• SetFit/amazon_reviews_multi_en metrics:
• accuracy pipeline_tag: text-classification tags:
• sentiment-analysis
• distilbert
• multi-class-classification

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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