transformers pytorch bert text-classification sarcasm-detection multilingual autotrain_compatible endpoints_compatible region:us
---
language: multilingual
tags:
- bert
- sarcasm-detection
- text-classification
widget:
- text: Gli Usa a un passo dalla recessione
- text: CIA Realizes It's Been Using Black Highlighters All These Years.
- text: We deden een man een nacht in een vat met cola en nu is hij dood
---

# Multilingual Sarcasm Detector

Multilingual Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) and the training data consists of ready-made datasets available on Kaggle as well scraped data from multiple newspapers in English, Dutch and Italian.


<b>Labels</b>: 
0 -> Not Sarcastic;
1 -> Sarcastic


## Source Data
	  
Datasets:
- English language data: [Kaggle: News Headlines Dataset For Sarcasm Detection](https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection).
- Dutch non-sarcastic data: [Kaggle: Dutch News Articles](https://www.kaggle.com/datasets/maxscheijen/dutch-news-articles)

Scraped data:
- Dutch sarcastic news from [De Speld](https://speld.nl)
- Italian non-sarcastic news from [Il Giornale](https://www.ilgiornale.it)
- Italian sarcastic news from [Lercio](https://www.lercio.it)

## Training Dataset
- [helinivan/sarcasm_headlines_multilingual](https://huggingface.co/datasets/helinivan/sarcasm_headlines_multilingual)

## 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/multilingual-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': 0.9374828934669495}
```

## Performance
| Model-Name | F1 | Precision | Recall | Accuracy 
| ------------- |:-------------| -----| -----| ----| 
| [helinivan/english-sarcasm-detector ](https://huggingface.co/helinivan/english-sarcasm-detector)| 92.38 | 92.75 | 92.38 | 92.42
| [helinivan/italian-sarcasm-detector ](https://huggingface.co/helinivan/italian-sarcasm-detector) | 88.26 | 87.66 | 89.66 | 88.69
| [helinivan/multilingual-sarcasm-detector ](https://huggingface.co/helinivan/multilingual-sarcasm-detector) | **87.23** | 88.65 | 86.33 | 88.30
| [helinivan/dutch-sarcasm-detector ](https://huggingface.co/helinivan/dutch-sarcasm-detector) | 83.02 | 84.27 | 82.01 | 86.81