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README.md

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+# **Fine-Tuned Sentiment Analysis Model**
+  
+[![Hugging Face Model](https://img.shields.io/badge/Hugging%20Face-Sentiment%20Model-yellow)](https://huggingface.co/ktr008/sentiment)  
+🔗 **Model ID:** `ktr008/sentiment`  
+📅 **Last Updated:** `2025-02-25`  
+🚀 **Framework:** PyTorch | Transformers  
+
+## **📌 Model Description**
+This is a **fine-tuned RoBERTa model** for **sentiment analysis** based on the **Twitter RoBERTa Base** model. It classifies text into **Positive, Neutral, or Negative** sentiments.  
+
+## **🛠️ How to Use**
+### **Install Dependencies**  
+```sh
+pip install transformers torch scipy
+```
+### **Load Model & Tokenizer**
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import numpy as np
+from scipy.special import softmax
+
+model = AutoModelForSequenceClassification.from_pretrained("ktr008/sentiment")
+tokenizer = AutoTokenizer.from_pretrained("ktr008/sentiment")
+
+def predict_sentiment(text):
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+    
+    labels = ["Negative", "Neutral", "Positive"]
+    ranking = np.argsort(scores)[::-1]
+    
+    return {labels[i]: round(float(scores[i]), 4) for i in ranking}
+
+# Example usage
+print(predict_sentiment("I love this product!"))
+```
+
+## **💡 Training Details**
+- **Base Model:** `cardiffnlp/twitter-roberta-base-sentiment-latest`  
+- **Dataset:** Custom dataset with labeled sentiment texts  
+- **Fine-Tuning:** Performed on AWS EC2 with PyTorch  
+- **Batch Size:** 16  
+- **Optimizer:** AdamW  
+- **Learning Rate:** 5e-5  
+
+## **📝 Example Predictions**
+| Text | Prediction |
+|-------|------------|
+| `"I love this product!"` | Positive (98.3%) |
+| `"It's an okay experience."` | Neutral (67.4%) |
+| `"I hate this! Never buying again."` | Negative (92.1%) |
+
+## **📈 Performance**
+- **Accuracy:** `0.9344`  
+
+## **📌 Model Limitations**
+- May struggle with **sarcasm or ambiguous phrases**.  
+- Can be **biased** towards the training dataset.  
+- Not suitable for **long texts** without truncation.  
+
+## **📍 Citation**
+If you use this model, please cite:  
+```
+@model{ktr008_sentiment_2025,
+  author    = {ktr008},
+  title     = {Fine-Tuned Sentiment Analysis Model},
+  year      = {2025},
+  publisher = {Hugging Face},
+  version   = {1.0}
+}
+```
+
+## **📬 Contact**
+For issues or suggestions, reach out to me on [Hugging Face](https://huggingface.co/ktr008) 🚀
+
+---