Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use Jongha611/ynat_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jongha611/ynat_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jongha611/ynat_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jongha611/ynat_model") model = AutoModelForSequenceClassification.from_pretrained("Jongha611/ynat_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Jongha611/news-classifier
Browse files
README.md
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library_name: transformers
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language:
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- ko
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tags:
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- text-classification
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- KoELECTRA
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# ynat_model
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- eval_loss: 2.0522
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- eval_accuracy: 0.0917
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library_name: transformers
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language:
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- ko
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license: apache-2.0
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base_model: monologg/koelectra-base-v3-discriminator
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tags:
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- text-classification
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- KoELECTRA
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# ynat_model
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This model is a fine-tuned version of [monologg/koelectra-base-v3-discriminator](https://huggingface.co/monologg/koelectra-base-v3-discriminator) on the klue-ynat dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 2.0522
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- eval_accuracy: 0.0917
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