metadata
license: apache-2.0
datasets:
- SetFit/amazon_reviews_multi_en
language:
- en
metrics:
- accuracy
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
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
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'}")