Text Classification
Transformers
Safetensors
Korean
electra
text_calssification
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use BigBigBe/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigBigBe/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BigBigBe/ynat-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BigBigBe/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("BigBigBe/ynat-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13849725aac2a70ce37bba718135f8c2860f9d84f262e276a175ba841f49648a
- Size of remote file:
- 5.3 kB
- SHA256:
- e040d5392a779afd0224ab4922d56d63f0c7c06b42a1824c336aa990720ff7e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.