readme: ---
  language: "it"
  tags:
  - bert
  - sarcasm-detection
  - text-classification
  widget:
  - text: "Gli Usa a un passo dalla recessione"
  ---
  
  # Italian Sarcasm Detector
  
  Italian Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on [dbmdz/bert-base-italian-uncased](https://huggingface.co/dbmdz/bert-base-italian-uncased) and the training data consists of scraped data from Italian non-sarcastic newspaper (Il Giornale) and sarcastic newspaper (Lercio).
  
  
  <b>Labels</b>: 
  0 -> Not Sarcastic;
  1 -> Sarcastic
  
  
  ## Source Data
  Scraped data:
  - 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/italian-sarcasm-detector"
  
  tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
  model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
  
  text = "Gli Usa a un passo dalla recessione"
  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': 0, 'confidence': 0.9965020418167114}
  ```
  
  ## 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