Update README.md

README.md

@@ -5,42 +5,22 @@ language:
 pipeline_tag: text-classification
 tags:
 - sentiment-analysis
+- transformers
+- huggingface
 ---
 
----
-language: en
-license: apache-2.0
-pipeline_tag: text-classification
-tags:
-  - sentiment-analysis
-  - mental-health
-  - transformers
-  - huggingface
-  - ai-chatbot
----
+# 🧠 Sentiment Analysis Model
 
-# 🧠 MindGuardAI – Sentiment Analysis Model
-
-This model is part of the **MindGuardAI** project, a mental health–focused chatbot system. It performs **sentiment analysis** on user input text to classify emotional tone as **positive** or **negative**, helping assess user mood and emotional state in real time.
-
-## ✨ Model Highlights
-
-- **Task**: Sentiment classification (binary: `Positive`, `Negative`)
-- **Trained On**: Social media mental health datasets (Twitter, Reddit-style samples)
-- **Model Type**: Fine-tuned BERT-based model using the Hugging Face `transformers` library
-- **Use Case**: Integrates with mental health chatbot systems for mood tracking and emotional awareness
-
----
+This model performs binary sentiment classification (Positive/Negative) on user-provided text inputs. It is trained to assist in mental health-related sentiment detection.
 
-## 🚀 Quick Start
+## 🚀 Usage
 
-### 🧪 Inference API
+You can try this model via the Hugging Face Inference API or integrate it in your application using the `transformers` library.
 
-Try it out instantly using Hugging Face's hosted API 👇
+## 🧪 Example
 
-```python
-from transformers import pipeline
+**Input:**  
+"I feel really hopeful today!"
 
-pipe = pipeline("text-classification", model="hasanmustafa0503/MindGuardAI")
-result = pipe("I feel really low and unmotivated today.")
-print(result)
+**Output:**  
+`Positive`