Text Classification
Transformers
Safetensors
Korean
electra
KoELECTRA
Korean-NLP
topic-classification
news-classification
Generated from Trainer
Instructions to use seoyeon111/ynat-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seoyeon111/ynat-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seoyeon111/ynat-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seoyeon111/ynat-model") model = AutoModelForSequenceClassification.from_pretrained("seoyeon111/ynat-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from seoyeon111/ynat-model: direct link, hf CLI and curl.
- Browser
- Download file 452 MB
-
https://huggingface.co/seoyeon111/ynat-model/resolve/main/model.safetensors
- Command line
-
hf download hf://seoyeon111/ynat-model/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/seoyeon111/ynat-model/resolve/main/model.safetensors
452 MB
- Xet hash:
- d6685928ef86430b00b2425cab5d261be7a23167657e968aa04743d5dc610c9a
- Size of remote file:
- 452 MB
- SHA256:
- 72d48fe33a2d73b63354705021c6b1e8317730ef173ea5c50be763e6d4630ebb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.