Instructions to use memorygreen/koelectra_naver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memorygreen/koelectra_naver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="memorygreen/koelectra_naver")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("memorygreen/koelectra_naver") model = AutoModelForSequenceClassification.from_pretrained("memorygreen/koelectra_naver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38c4669c48cbccbb76871251a7350dd22d9e492cf3a4ba896cae33e8aa1291f4
- Size of remote file:
- 5.2 kB
- SHA256:
- ffbebcef4d6656eca2b7059bfba1471fc748e6dfafb6e4dd507d55ecfb0542c0
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