File size: 2,543 Bytes
3d6ad07 e811c99 3d6ad07 b0d7cc2 1d99ed7 3d6ad07 b3e66ec 3d6ad07 1d99ed7 f6d0624 72d2fea 3d6ad07 72d2fea 3d6ad07 7bbcd80 3d6ad07 7bbcd80 3d6ad07 5cda127 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
language: "en"
tags:
- bert
- sarcasm-detection
- text-classification
widget:
- text: "CIA Realizes It's Been Using Black Highlighters All These Years."
---
# English Sarcasm Detector
English Sarcasm Detector is a text classification model built to detect sarcasm from news article titles. It is fine-tuned on [bert-base-uncased](https://huggingface.co./bert-base-uncased) and the training data consists of ready-made dataset available on Kaggle.
<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).
## 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/english-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': 0.9337034225463867}
```
## 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 |