Instructions to use madatnlp/km-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madatnlp/km-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="madatnlp/km-bert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("madatnlp/km-bert") model = AutoModel.from_pretrained("madatnlp/km-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a959a3cdc202fe48080f39d08dbc63202fc4f991ce16ba4992e2d2f672e5d35
- Size of remote file:
- 395 MB
- SHA256:
- ea3ff4c4506350056cf8b6d0e525be1f259ad69d8b62fc0801a14fd4a8eed4f7
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