Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use troutpiegalore/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use troutpiegalore/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="troutpiegalore/ynat-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("troutpiegalore/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("troutpiegalore/ynat-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcd0a023443daae1d076a67f5141de9a16b33483a9fd180f586b56105a520500
- Size of remote file:
- 5.84 kB
- SHA256:
- 1dbe8553d598256bd6c06b6392d588701c40b8a32b780c6b6c96e1c937eee827
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