Instructions to use Hate-speech-CNERG/dehatebert-mono-indonesian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hate-speech-CNERG/dehatebert-mono-indonesian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hate-speech-CNERG/dehatebert-mono-indonesian")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hate-speech-CNERG/dehatebert-mono-indonesian") model = AutoModelForSequenceClassification.from_pretrained("Hate-speech-CNERG/dehatebert-mono-indonesian") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This model is used detecting hatespeech in Indonesian language. The mono in the name refers to the monolingual setting, where the model is trained using only Arabic language data. It is finetuned on multilingual bert model. The model is trained with different learning rates and the best validation score achieved is 0.844494 for a learning rate of 2e-5. Training code can be found at this url
For more details about our paper
Sai Saketh Aluru, Binny Mathew, Punyajoy Saha and Animesh Mukherjee. "Deep Learning Models for Multilingual Hate Speech Detection". Accepted at ECML-PKDD 2020.
Please cite our paper in any published work that uses any of these resources.
@article{aluru2020deep,
title={Deep Learning Models for Multilingual Hate Speech Detection},
author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2004.06465},
year={2020}
}
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