Instructions to use w11wo/javanese-bert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use w11wo/javanese-bert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="w11wo/javanese-bert-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("w11wo/javanese-bert-small") model = AutoModelForMaskedLM.from_pretrained("w11wo/javanese-bert-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from w11wo/javanese-bert-small: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/w11wo/javanese-bert-small/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://w11wo/javanese-bert-small/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/w11wo/javanese-bert-small/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 17d7fa946531505b7b3294cc2fdc4da8820aa58d3aee232c3b45eab0232c523e
- Size of remote file:
- 438 MB
- SHA256:
- 715f2a7e45b790077df22d3aa40144a9f86fa315bbec969c0f1278e39b5e9cbd
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