Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use Seonghaa/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Seonghaa/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Seonghaa/ynat-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Seonghaa/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("Seonghaa/ynat-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56703dc1b7688234144a81afceffb8244f7bc4a0e82af40b58fbe2ce4d868130
- Size of remote file:
- 5.3 kB
- SHA256:
- 3f354881ee9302b40c1b19703b075945a03860f0fa6d08a0b4ff89ff8682e05e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.