readme: ---
  language: "nl"
  tags:
  - bert
  - sarcasm-detection
  - text-classification
  widget:
  - text: "We deden een man een nacht in een vat met cola en nu is hij dood"
  ---
  
  # Dutch Sarcasm Detector
  
  Dutch Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) and the training data consists of ready-made dataset available on Kaggle as well as scraped data from Dutch sarcastic newspaper (De Speld).
  
  
  <b>Labels</b>: 
  0 -> Not Sarcastic;
  1 -> Sarcastic
  
  ## Source Data
  	  
  Datasets:
  - 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)
  
  
  ## 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/dutch-sarcasm-detector"
  
  tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
  model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
  
  text = "We deden een man een nacht in een vat met cola en nu is hij dood"
  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': 0.8915400505065918}
  ```
  
  ## 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