Instructions to use cahya/bert-base-indonesian-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/bert-base-indonesian-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cahya/bert-base-indonesian-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cahya/bert-base-indonesian-NER") model = AutoModelForTokenClassification.from_pretrained("cahya/bert-base-indonesian-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from cahya/bert-base-indonesian-NER: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/cahya/bert-base-indonesian-NER/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://cahya/bert-base-indonesian-NER/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/cahya/bert-base-indonesian-NER/resolve/main/flax_model.msgpack
440 MB
- Xet hash:
- 04d385246db8a0fb6f0049bc7ab1a2dcd487ed4e9bdaa14af7ff8a9962e8953a
- Size of remote file:
- 440 MB
- SHA256:
- 0e9285fce69980929baa88f49c41b790420e1fa29158b16c32999a88668a22bd
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