Instructions to use BM-K/KoMiniLM-68M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BM-K/KoMiniLM-68M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BM-K/KoMiniLM-68M")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BM-K/KoMiniLM-68M") model = AutoModelForSequenceClassification.from_pretrained("BM-K/KoMiniLM-68M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d4078cf104f5e699db2c8cf444ddebd4b4f3bf5b8919cf79f87f386bd451f45
- Size of remote file:
- 272 MB
- SHA256:
- c7f1c809b86dd681d2e80ddce03b5120cc87271693c3cc368fe8817d6532ec78
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