YamenRM/sarcasm_model

DistilBERT for Sarcasm Detection 🎭

This is a fine-tuned DistilBERT model on the News Headlines Dataset for Sarcasm Detection.

📊 Dataset

• Source: 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

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 .