Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Indonesian
bert
indobert
indobenchmark
indonlu
Instructions to use indobenchmark/indobert-large-p1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indobenchmark/indobert-large-p1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="indobenchmark/indobert-large-p1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-large-p1") model = AutoModel.from_pretrained("indobenchmark/indobert-large-p1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from indobenchmark/indobert-large-p1: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/indobenchmark/indobert-large-p1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://indobenchmark/indobert-large-p1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/indobenchmark/indobert-large-p1/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- c82ee6ff7af7aee78120622548f336a30ee208522171dbcd9f2131be8f5d0d18
- Size of remote file:
- 1.34 GB
- SHA256:
- 7d549e4f3dcaa79dc88d8c864f4e30ffb239209395f0e413da54683d580ce61e
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