Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Indonesian
bert
indobert
indobenchmark
indonlu
Instructions to use indobenchmark/indobert-large-p2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indobenchmark/indobert-large-p2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="indobenchmark/indobert-large-p2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-large-p2") model = AutoModel.from_pretrained("indobenchmark/indobert-large-p2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae77d9c650ee749d0d9709836760a5e99f1bcdcb5d218c6579687466be38d56d
- Size of remote file:
- 1.34 GB
- SHA256:
- adb98dec62ea217e729579642017fde2aa41c87a19adfd0da4049de676190533
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