Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Indonesian
bert
indobert
indobenchmark
indonlu
Instructions to use indobenchmark/indobert-base-p2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indobenchmark/indobert-base-p2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="indobenchmark/indobert-base-p2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p2") model = AutoModel.from_pretrained("indobenchmark/indobert-base-p2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from indobenchmark/indobert-base-p2: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/indobenchmark/indobert-base-p2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://indobenchmark/indobert-base-p2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/indobenchmark/indobert-base-p2/resolve/main/pytorch_model.bin
498 MB
- Xet hash:
- 2c00c23ed101225f55f3aca89718e6b4ddb8aa71a9e02c160f3433c153aedfb5
- Size of remote file:
- 498 MB
- SHA256:
- 37ab1a0f22e5d474658a762c3e538199757bfa481958dc3169a36a4a508c6b6f
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