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--- |
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language: "multilingual" |
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tags: |
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- bert |
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- sarcasm-detection |
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- text-classification |
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widget: |
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- text: "Gli Usa a un passo dalla recessione" |
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- text: "CIA Realizes It's Been Using Black Highlighters All These Years." |
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- text: "We deden een man een nacht in een vat met cola en nu is hij dood" |
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--- |
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# Multilingual Sarcasm Detector |
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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. |
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<b>Labels</b>: |
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0 -> Not Sarcastic; |
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1 -> Sarcastic |
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## Training Data |
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Datasets: |
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- English language data: [Kaggle: News Headlines Dataset For Sarcasm Detection](https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection). |
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- Dutch non-sarcastic data: [Kaggle: Dutch News Articles](https://www.kaggle.com/datasets/maxscheijen/dutch-news-articles) |
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Scraped data: |
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- Dutch sarcastic news from [De Speld](https://speld.nl) |
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- Italian non-sarcastic news from [Il Giornale](https://www.ilgiornale.it) |
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- Italian sarcastic news from [Lercio](https://www.lercio.it) |
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Codebase: |
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- Git Repo: [Official repository](https://github.com/helinivan/multilingual-sarcasm-detector) |
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--- |
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## Example of classification |
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```python |
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from transformers import AutoModelForSequenceClassification |
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from transformers import AutoTokenizer |
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import string |
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def preprocess_data(text: str) -> str: |
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return text.lower().translate(str.maketrans("", "", string.punctuation)).strip() |
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MODEL_PATH = "helinivan/multilingual-sarcasm-detector" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) |
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model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH) |
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text = "CIA Realizes It's Been Using Black Highlighters All These Years." |
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tokenized_text = tokenizer([preprocess_data(text)], padding=True, truncation=True, max_length=256, return_tensors="pt") |
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output = model(**tokenized_text) |
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probs = output.logits.softmax(dim=-1).tolist()[0] |
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confidence = max(probs) |
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prediction = probs.index(confidence) |
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results = {"is_sarcastic": prediction, "confidence": confidence} |
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``` |
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Output: |
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``` |
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{'is_sarcastic': 1, 'confidence': 0.9374828934669495} |
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``` |
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## Performance |
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| Model-Name | F1 | Precision | Recall | Accuracy |
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| ------------- |:-------------| -----| -----| ----| |
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| [helinivan/english-sarcasm-detector ](https://huggingface.co/helinivan/english-sarcasm-detector)| 92.38 | 92.75 | 92.38 | 92.42 |
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| [helinivan/italian-sarcasm-detector ](https://huggingface.co/helinivan/italian-sarcasm-detector) | 88.26 | 87.66 | 89.66 | 88.69 |
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| [helinivan/multilingual-sarcasm-detector ](https://huggingface.co/helinivan/multilingual-sarcasm-detector) | **87.23** | 88.65 | 86.33 | 88.30 |
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| [helinivan/dutch-sarcasm-detector ](https://huggingface.co/helinivan/dutch-sarcasm-detector) | 83.02 | 84.27 | 82.01 | 86.81 |
