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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-large-p1") model = AutoModel.from_pretrained("indobenchmark/indobert-large-p1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e866de7f7324884a8744d7d5e40b207d389dfecd39772306ee3cc63475452193
- Size of remote file:
- 1.34 GB
- SHA256:
- 4e93e30a608633c7492a15ed3c4afd50c8c5f713d1bf36d8a3bac3ffaf915f2d
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