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

Model Overview

• Base Model: google-bert/bert-base-uncased
• Dataset: SetFit/amazon_reviews_multi_en,
• Classes: Binary classification (Positive, Negative)
• Performance:
  • Test Accuracy: 89%
  • Validation Accuracy: 89%

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 BertTokenizer, BertForSequenceClassification
import torch
import streamlit as st

# Load the tokenizer and model
tokenizer = BertTokenizer.from_pretrained(
    "ashish-001/Bert-Amazon-review-sentiment-classifier")
model = BertForSequenceClassification.from_pretrained(
    "ashish-001/Bert-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 == 1 else 'Negative'}")