Nav772/roberta-amazon-reviews-5star

license: mit language: en datasets:

• —SetFit/amazonreviewsmulti_en metrics:
• —accuracy pipeline_tag: text-classification tags:
• —sentiment-analysis
• —roberta
• —multi-class-classification ---

RoBERTa Fine-tuned on Amazon Reviews (5-Star Rating)

Model Description

This model is a fine-tuned version of roberta-base for 5-class sentiment classification, predicting star ratings (1-5) from Amazon product reviews.

Comparison with DistilBERT

This model was trained as part of a model comparison study:

RoBERTa provides ~5 percentage points higher accuracy at the cost of slower inference.

Training Data

• —Dataset: SetFit/amazonreviewsmulti_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: roberta-base
• —Epochs: 3
• —Batch size: 16
• —Learning rate: 2e-5
• —Max sequence length: 256

Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="Nav772/roberta-amazon-reviews-5star")
result = classifier("This product exceeded my expectations! Great quality.")
print(result)

When to Use This Model

• —Choose RoBERTa when accuracy is the priority and latency is less critical
• —Choose DistilBERT when you need faster inference or have resource constraints

Demo

Try the model comparison demo: sentiment-model-comparison

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
• —English language only