Instructions to use yosuke/bert-base-japanese-char with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yosuke/bert-base-japanese-char with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yosuke/bert-base-japanese-char")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yosuke/bert-base-japanese-char") model = AutoModelForMaskedLM.from_pretrained("yosuke/bert-base-japanese-char", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from yosuke/bert-base-japanese-char: direct link, hf CLI and curl.
- Browser
- Download file 241 MB
-
https://huggingface.co/yosuke/bert-base-japanese-char/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://yosuke/bert-base-japanese-char/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/yosuke/bert-base-japanese-char/resolve/main/flax_model.msgpack
241 MB
- Xet hash:
- c4cfe04d30c42b7454ccb2782784cea6e7a87a467a73d96bfb840688d25f3e97
- Size of remote file:
- 241 MB
- SHA256:
- 94d19cb1f242ed020b73ae9bbcecc49bffe38305225f6f9c3156e97339a5651a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.