Instructions to use chaimag/ko_bert_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chaimag/ko_bert_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="chaimag/ko_bert_trained")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("chaimag/ko_bert_trained") model = AutoModel.from_pretrained("chaimag/ko_bert_trained") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3df647bc80d13f171f0a3232f254be38e19308b6ac8b4754f26e843d9be4736
- Size of remote file:
- 473 MB
- SHA256:
- cf52de745ba69bd1e097f419d038c976ce91979476daaa86e2b20357eb000ca7
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