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

# 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-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.

## Metrics:


## 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]).
- 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])

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=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': 0.9999909400939941}
```

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

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "bert-base-multilingual-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "bert-base-multilingual-uncased",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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