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