---
language: "en"
tags:
- bert
- sarcasm-detection
- text-classification
widget:
- text: "CIA Realizes It's Been Using Black Highlighters All These Years."
---

# English Sarcasm Detector

English Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on bert-base-uncased and the training data consists of ready-made dataset available on Kaggle.


## Training Data

Datasets:
- English language data: [Kaggle: News Headlines Dataset For Sarcasm Detection]([https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection]).

Codebase:
- Git Repo: [Official repository]([https://github.com/helinivan/multilingual-sarcasm-detector]).

---

## Example of classification

```python
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=512, 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': 0.9997416138648987}
```

## Performance
| Model-Name | F1 | Precision | Recall | Accuracy 
| ------------- |:-------------| -----| -----| ----| 
| helinivan/english-sarcasm-detector | 94.48 | 94.46 | 94.51 | 94.48
| helinivan/multilingual-sarcasm-detector | 90.91 | 91.51 | 90.44 | 91.55