English Sarcasm Detector

🚀 English Sarcasm Detector

The English Sarcasm Detector is a text classification model designed to detect sarcasm in news article titles. It is fine - tuned on [bert - base - uncased](https://huggingface.co/bert - base - uncased) and trained with a ready - made dataset from Kaggle.

Labels

• 0 -> Not Sarcastic
• 1 -> Sarcastic

🚀 Quick Start

The English Sarcasm Detector is a powerful tool for identifying sarcasm in English news headlines. It leverages the pre - trained bert - base - uncased model and is fine - tuned on specific datasets.

✨ Features

• Sarcasm Detection: Accurately detects sarcasm in English news article titles.
• Fine - Tuned Model: Based on the well - known bert - base - uncased model.
• Diverse Datasets: Trained on datasets from Kaggle and Hugging Face.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import string

def preprocess_data(text: str) -> str:
   return text.lower().translate(str.maketrans("", "", string.punctuation)).strip()

MODEL_PATH = "helinivan/english-sarcasm-detector"

tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)

text = "CIA Realizes It's Been Using Black Highlighters All These Years."
tokenized_text = tokenizer([preprocess_data(text)], padding=True, truncation=True, max_length=256, return_tensors="pt")
output = model(**tokenized_text)
probs = output.logits.softmax(dim=-1).tolist()[0]
confidence = max(probs)
prediction = probs.index(confidence)
results = {"is_sarcastic": prediction, "confidence": confidence}

Output:

{'is_sarcastic': 1, 'confidence': 0.9337034225463867}

📚 Documentation

Source Data

Performance