Feature Extraction
Transformers
PyTorch
TensorFlow
JAX
Indonesian
bert
indobert
indobenchmark
indonlu
Instructions to use indobenchmark/indobert-base-p1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indobenchmark/indobert-base-p1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="indobenchmark/indobert-base-p1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p1") model = AutoModel.from_pretrained("indobenchmark/indobert-base-p1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 194df6cc2e8d3a45202f57307f1ca56e9a0ac36bc6ee9dcbcd37f042227dbaa8
- Size of remote file:
- 656 MB
- SHA256:
- 95edc7285ff4a6d38b24f1af01b79115a3229f0b6693d63e273197ffc8c1c4e6
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