Update README.md

README.md

@@ -1,3 +1,65 @@
----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+---
+# IndoBERTweet-SexuallyExplicit
+
+## Model Description
+IndoBERTweet fine-tuned on IndoToxic2024 dataset, with an accuracy of 0.91 and macro-F1 of 0.80. 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-SexuallyExplicit"
+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-SexuallyExplicit
+Text 1: billiard engak ntar bro?
+Prediction: 0
+Text 2: eh kerumah ku yok main bareng di ranjang
+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}, 
+}
+```