---
license: apache-2.0
language:
- en
---
# DistilBERT for Sarcasm Detection 🎭

This is a fine-tuned [DistilBERT](https://huggingface.co/distilbert-base-uncased) model on the **News Headlines Dataset for Sarcasm Detection**.

## 📊 Dataset
- **Source:** [News Headlines Dataset for Sarcasm Detection](https://www.kaggle.com/rmisra/news-headlines-dataset-for-sarcasm-detection)
- **Task:** Binary classification (`0 = Not Sarcastic`, `1 = Sarcastic`)
- **Size:** ~28,000 headlines

## 🧠 Model Training
- Framework: Hugging Face Transformers
- Tokenizer: `distilbert-base-uncased`
- Training epochs: 3
- Optimizer: AdamW
- Batch size: 16

## 📈 Performance
| Model        | Accuracy |
|--------------|----------|
| **DistilBERT (ours)** | **93.1%** |
| GRU          | 85.3% |
| LSTM         | 84.6% |
| Logistic Regression | 83.4% |
| SVM          | 82.9% |
| Naive Bayes  | 82.7% |

## 🚀 Usage

```python
from transformers import pipeline

# Load the model from HF Hub
classifier = pipeline("text-classification", model="YamenRM/sarcasm_model")

# Example
text = "Oh great, another Monday morning meeting!"
print(classifier(text))
```
### Output:
[{'label': 'SARCASTIC', 'score': 0.93}]

## ✨ Author

Trained and uploaded by **YamenRM .**