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

---

# **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) 🚀

---