This model is a fine-tuned version of distilbert-base-uncased on an Amazon product reviews dataset. It classifies customer reviews into two sentiment categories: Negative (label 0): rating < 3.5 Positive (label 1): rating ≥ 3.5 The model is designed to support automated customer service systems by providing real-time sentiment analysis.

Developed by: Estella
Model type: Text Classification
Language(s) (NLP): English
License: apache-2.0
Number of Classes: 2
- 0: Negative
- 1: Positive

Intended Uses & Limitations

Intended Use:

Sentiment analysis for e-commerce customer reviews
Pre-processing step for automated reply generation or customer feedback dashboard

Limitations:

The model was trained on product reviews from Amazon (electronics, cables, TVs, etc.). Performance on other domains (e.g., clothing, books) may vary.
It does not detect neutral sentiment; reviews with rating 3.5 are considered positive by the chosen threshold.

How to Use the Model

You can use this model directly with the Transformers pipeline for text classification.

python

from transformers import pipeline

classifier = pipeline("text-classification", model="your_username/amazon-sentiment-distilbert")
result = classifier("This product is amazing!")
print(result)  # [{'label': 'POSITIVE', 'score': 0.99}]

EBSQ/amazon-sentiment-distilbert

Model Card for Model ID

Model Details

Model Description

Intended Uses & Limitations

How to Use the Model

EBSQ/
amazon-sentiment-distilbert