ashish-001/DistilBert-Amazon-review-sentiment-classifier

This repository contains a fine-tuned DistilBERT model for sentiment classification of Amazon product reviews The model classifies a given review into two classes: Positive and Negative

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Model Overview

• Base Model: distilbert/distilbert-base-uncased
• Dataset: SetFit/amazon_reviews_multi_en,
• Classes: Binary classification (Positive, Negative)
• Performance:
  • Test Accuracy: 90%
  • Validation Accuracy: 90%

Figure 1: Confusion matrix for test data

Figure 2: Confusion matrix for validation data

How to Use the Model

Below is an example of how to load and use the model for sentiment classification:

from transformers import DistilBertTokenizer,DistilBertForSequenceClassification
import torch

# Load the tokenizer and model
model = DistilBertForSequenceClassification.from_pretrained(
    "ashish-001/DistilBert-Amazon-review-sentiment-classifier")
tokenizer = DistilBertTokenizer.from_pretrained(
    "ashish-001/DistilBert-Amazon-review-sentiment-classifier")

# Example usage
text = "This product is amazing!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
sentiment = torch.argmax(logits, dim=1).item()

print(f"Predicted sentiment: {'Positive' if sentiment else 'Negative'}")