Instructions to use nayohyun/koelectra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nayohyun/koelectra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nayohyun/koelectra")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nayohyun/koelectra") model = AutoModelForSequenceClassification.from_pretrained("nayohyun/koelectra") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4ab84a715348bb92b4a9a1ddc38f5f6807f375cff2acf9aaa040cc1194f86a0
- Size of remote file:
- 5.2 kB
- SHA256:
- 57aa1363571a8c4583f81faf15841d101a59c826b93b2ce54ba0e1f918887b1d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.