Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language: en
+license: apache-2.0
+tags:
+  - sentiment-analysis
+  - text-classification
+  - bert
+  - transformers
+  - news
+  - reviews
+---
+
+# Sentify-BERT — 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**.
+
+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.
+
+---
+
+## 🔍 Model Highlights
+
+- **Base model**: `bert-base-uncased`
+- **Fine-tuned for**: Sentiment classification (3-class)
+- **Accuracy**: > 86%
+- **Classes**: Positive, Neutral, Negative
+- **Language**: English
+- **Format**: `safetensors`
+- **Tokenizer**: Compatible with `bert-base-uncased`
+
+---
+
+## 💼 Applications
+
+This model is well-suited for:
+
+- **News article sentiment analysis**
+- **Amazon product review analysis**
+- **Customer support or service feedback systems**
+- **General-purpose opinion mining**
+
+---
+
+## 🚀 Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained("your-username/Sentify-BERT")
+tokenizer = AutoTokenizer.from_pretrained("your-username/Sentify-BERT")
+
+text = "The government’s response to the crisis was surprisingly effective."
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class = torch.argmax(logits, dim=1).item()
+print(["Negative", "Neutral", "Positive"][predicted_class])