metadata
language: en
license: apache-2.0
tags:
- sentiment-analysis
- text-classification
model_name: Your Model Name
datasets: dataset-used
Fine-Tuned Sentiment Analysis Model
🔗 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
pip install transformers torch scipy
Load Model & Tokenizer
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 🚀