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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from indobenchmark/indobert-large-p2: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/indobenchmark/indobert-large-p2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://indobenchmark/indobert-large-p2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/indobenchmark/indobert-large-p2/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- 45dafeab9c4fac8bb0403b42162c10ffa71dd40b1221659f501ef0fc3ac6a1c0
- Size of remote file:
- 1.34 GB
- SHA256:
- 321d78e9050b6713359df658ec89ec78e56dac099fc56f237855f4b6a136c8a5
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