Improve README formatting and add model link

distilbert-news-classifier-dheeraj is a lightweight and efficient text classification model fine-tuned from the distilbert-base-uncased transformer model. It is designed to categorize short news headlines into one of four major classes:

Sports

Business

Tech

Science

This project demonstrates how to quickly build and deploy a custom NLP classifier using the Hugging Face 🤗 ecosystem, including transformers, datasets, and Trainer.

The model was trained on a small synthetic dataset inspired by the AG News classification structure. Despite its limited size, the model showcases the capabilities of DistilBERT in low-resource settings and serves as a solid foundation for educational purposes or further fine-tuning with larger datasets.

README.md

@@ -1,15 +1,30 @@
+---
+license: apache-2.0
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- distilbert/distilbert-base-uncased-finetuned-sst-2-english
+pipeline_tag: text-classification
+library_name: transformers
+---
 # 📚 DistilBERT News Classifier by Dheeraj
 
 A text classification model fine-tuned on news headlines to classify them into one of four categories:
 
-- 0 → Sports  
-- 1 → Business  
-- 2 → Tech  
-- 3 → Science
+- **0 → Sports**  
+- **1 → Business**  
+- **2 → Tech**  
+- **3 → Science**
+
+---
 
 ## 🧠 Model Info
 
-This model is based on `distilbert-base-uncased` and trained using Hugging Face's `Trainer`.
+This model is based on [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) and trained using Hugging Face's `Trainer` API.
+
+---
 
 ## 🚀 Example Usage
 
@@ -18,5 +33,4 @@ from transformers import pipeline
 
 classifier = pipeline("text-classification", model="Dheeraj3103/distilbert-news-classifier-dheeraj")
 result = classifier("NASA finds evidence of water on Mars.")
-print(result)
-
+print(result)