Update README.md

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) 🚀
-
+---
+language: en
+license: apache-2.0
+tags:
+  - sentiment-analysis
+  - text-classification
+model_name: "Your Model Name"
+datasets: "dataset-used"
 ---