Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- SetFit/amazon_reviews_multi_en
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- distilbert/distilbert-base-uncased
+pipeline_tag: text-classification
+---
+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
+import streamlit as st
+
+# Load the tokenizer and model
+tokenizer = DistilBertForSequenceClassification.from_pretrained(
+    "ashish-001/DistilBert-Amazon-review-sentiment-classifier")
+model = 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'}")