Instructions to use indobenchmark/indobert-lite-base-p1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indobenchmark/indobert-lite-base-p1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="indobenchmark/indobert-lite-base-p1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-lite-base-p1") model = AutoModel.from_pretrained("indobenchmark/indobert-lite-base-p1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ddd34f6a66ade45f8918bc902f6bdc2a5ed5ec81797484c7177318da0f0fe63d
- Size of remote file:
- 46.7 MB
- SHA256:
- 5e934d3f0a095dff24acf3349458c72ae1297ca71f92f70e70e3430d005018ac
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