Instructions to use memorygreen/kobart_naver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memorygreen/kobart_naver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="memorygreen/kobart_naver")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("memorygreen/kobart_naver") model = AutoModelForSequenceClassification.from_pretrained("memorygreen/kobart_naver", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 758d6dc9b1ae71fc15f7f3c36e47d9defb9171e61cdc2b2807aa66b6f6433061
- Size of remote file:
- 5.2 kB
- SHA256:
- 919a410a1ba7c8e4acd92e318c404ce9bfb2448d3a07ab5dd91c267a3ba66632
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