metadata
license: mit
datasets:
- haipradana/indonesian-twitter-hate-speech-cleaned
language:
- id
tags:
- bert
- RoBERTa
- tweet
- hate
- twitter
base_model:
- cardiffnlp/twitter-roberta-base-sentiment-latest
Fine-tuned RoBERTa pre-trained model to classify Indonesian hate tweet(s)
Just check GitHub for full-code and Google Colab: https://github.com/haipradana/RoBERTa-Indonesian-Hate-Tweet-Classification/tree/main
This project fine-tunes a RoBERTa model from cardiffnlp/twitter-roberta-base-sentiment-latest to classify Indonesian tweets as either neutral or hate speech.
How to use this model?
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model
tokenizer = AutoTokenizer.from_pretrained('./model')
model = AutoModelForSequenceClassification.from_pretrained('./model')
# Predict
def predict(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=511)
with torch.no_grad():
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=1).item()
return 'hate' if prediction == 1 else 'neutral'
# Example
result = predict("Paru-parumu terbuat dari batu ya? udah sakit gini masih aja merokok!")
print(result) # Output: hate
Or just using the script in the GitHub Repos
cd scripts
python predict.py
Performance Metrics
Accuracy: 82.01%
Precision: 82.68%
Recall: 81.72%
F1-Score: 82.19%