Instructions to use JunHwi/kmhas_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHwi/kmhas_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunHwi/kmhas_binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JunHwi/kmhas_binary") model = AutoModelForSequenceClassification.from_pretrained("JunHwi/kmhas_binary") - Notebooks
- Google Colab
- Kaggle
| Pretrained K-mHas with binary-label model with "koelectra-v3" | |
| You can use tokenizer of this model with "monologg/koelectra-v3-base-discriminator" | |
| dataset : https://huggingface.co/datasets/jeanlee/kmhas_korean_hate_speech | |
| pretrained_model : https://huggingface.co/monologg/koelectra-base-v3-discriminator | |
| label maps are like this. | |
| > | |
| {0: "not_hate_speech", 1: "hate_speech"} |