Update README.md

README.md

@@ -10,11 +10,11 @@ tags:
   - reviews
 ---
 
-# Sentify-BERT — Fine-tuned BERT for Sentiment Classification (Positive, Neutral, Negative)
+# SentimentBERT — Fine-tuned BERT for Sentiment Classification (Positive, Neutral, Negative)
 
-**Sentify-BERT** is a BERT-based model specifically fine-tuned for **sentiment classification of sentences** into three categories: **Positive**, **Negative**, and **Neutral**.
+**SentimentBERT** is a Finetuned BERT-based model specifically for **sentiment classification of sentences** into three categories: **Positive**, **Negative**, and **Neutral**.
 
-This model has been trained on a **large and diverse dataset of news articles** across a wide range of categories. It achieves **over 86% accuracy** and demonstrates a strong understanding of sentence-level sentiment, even in nuanced or mixed-context cases.
+This model has been trained on a ** 130K large and diverse dataset of news articles** across a wide range of categories. It achieves **over 86% accuracy** and demonstrates a strong understanding of sentence-level sentiment, even in nuanced or mixed-context cases.
 
 ---
 
@@ -47,8 +47,8 @@ This model is well-suited for:
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-model = AutoModelForSequenceClassification.from_pretrained("your-username/Sentify-BERT")
-tokenizer = AutoTokenizer.from_pretrained("your-username/Sentify-BERT")
+model = AutoModelForSequenceClassification.from_pretrained("mervp/SentimentBERT")
+tokenizer = AutoTokenizer.from_pretrained("mervp/SentimentBERT")
 
 text = "The government’s response to the crisis was surprisingly effective."
 inputs = tokenizer(text, return_tensors="pt")