README.md

---
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](https://huggingface.co/distilbert/distilbert-base-uncased)
- **Dataset**: [SetFit/amazon_reviews_multi_en](https://huggingface.co/datasets/SetFit/amazon_reviews_multi_en),
- **Classes**: Binary classification (`Positive`, `Negative`)
- **Performance**:
  - **Test Accuracy**: 90%
  - **Validation Accuracy**: 90%

*Figure 1: Confusion matrix for test data*
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ab80ef59559493941225/IXLTT970lQzQqeGHNbkv6.png)


*Figure 2: Confusion matrix for validation data*

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585ab80ef59559493941225/JhE5eZPpHCxXEJkdaKRyI.png)

### How to Use the Model

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

```python
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'}")