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---
license: cc-by-sa-4.0
---
# IndoBERTweet-Profanity
## Model Description
IndoBERTweet fine-tuned on IndoToxic2024 dataset, with an accuracy of 0.81 and macro-F1 of 0.70. Performances are obtained through stratified 10-fold cross-validation.
## Supported Tokenizer
- **indolem/indobertweet-base-uncased**
## Example Code
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Specify the model and tokenizer name
model_name = "Exqrch/IndoBERTweet-Profanity"
tokenizer_name = "indolem/indobertweet-base-uncased"
# Load the pre-trained model
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
text = "selamat pagi semua!"
output = model(**tokenizer(text, return_tensors="pt"))
logits = output.logits
# Get the predicted class label
predicted_class = torch.argmax(logits, dim=-1).item()
print(predicted_class)
--- Output ---
> 0
--- End of Output ---
```
## Limitations
Trained only on Indonesian texts. No information on code-switched text performance.
## Sample Output
```
Model name: Exqrch/IndoBERTweet-Profanity
Text 1: aku butuh bantuan nih buat belajar, pc yang ingin bantu
Prediction: 0
Text 2: sumpah, tolol banget dah anjing ini matkul
Prediction: 1
```
## Citation
If used, please cite:
```
@article{susanto2024indotoxic2024,
title={IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language},
author={Lucky Susanto and Musa Izzanardi Wijanarko and Prasetia Anugrah Pratama and Traci Hong and Ika Idris and Alham Fikri Aji and Derry Wijaya},
year={2024},
eprint={2406.19349},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.19349},
}
```
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