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amazon-reviews-roberta
aablaess/amazon-reviews-roberta
amazon-reviews-roberta is a text classification model from aablaess. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This is a fine-tuned RoBERTa model for Sentiment Analysis, trained on the Amazon Fine Food Reviews dataset. It classifies English food reviews into three categories: positive, neutral, and negative.
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- May 22, 2026
- Updated
- May 24, 2026
- SHA
- 0fc491be
Try a prompt
Model card
From the Hugging Face model README
Amazon Fine Food Reviews - RoBERTa Sentiment Analysis
Model Description
This is a fine-tuned RoBERTa model for Sentiment Analysis, trained on the Amazon Fine Food Reviews dataset. It classifies English food reviews into three categories: positive, neutral, and negative.
This model is part of an academic Web Mining project developed at EMSI Marrakech.
Performance
- Accuracy: ~84%
- F1-Score (Macro): ~0.84
Intended Uses
- Intended Use: Analyzing customer sentiment in e-commerce food reviews.
- Language: English
How to use
You can use this model directly with a pipeline for text classification:
from transformers import pipeline
# Load the model
sentiment_classifier = pipeline("text-classification", model="aablaess/amazon-reviews-roberta")
# Analyze a review
result = sentiment_classifier("This product is absolutely delicious, I will buy it again!")
print(result)
# [{'label': 'positive', 'score': 0.98...}]
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Languages
- en
- Created
- May 22, 2026
- Updated
- May 24, 2026