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")# pip install -U transformers accelerate # 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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://indobenchmark/indobert-base-p2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/indobenchmark/indobert-base-p2/resolve/main/flax_model.msgpack
498 MB
- Xet hash:
- 947c71af3acb1bf7db6f66005e4a08433e3bf71c251191203eb35a2883d83616
- Size of remote file:
- 498 MB
- SHA256:
- 85b95beb12fb38e2bc2e16f8f55483ba18566d8686190fac5739843f7b80f55f
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