Fill-Mask
Transformers
PyTorch
Safetensors
gpt_bert
feature-extraction
gpt-bert
babylm
remote-code
custom_code
Instructions to use jumelet/gptbert-jpn-250steps-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jumelet/gptbert-jpn-250steps-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jumelet/gptbert-jpn-250steps-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jumelet/gptbert-jpn-250steps-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jumelet/gptbert-jpn-250steps-base: direct link, hf CLI and curl.
- Browser
- Download file 503 MB
-
https://huggingface.co/jumelet/gptbert-jpn-250steps-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jumelet/gptbert-jpn-250steps-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jumelet/gptbert-jpn-250steps-base/resolve/main/pytorch_model.bin
503 MB
- Xet hash:
- 3b5aea53fd2081ec4a10b03280ea39b8d5c29da98c56eaf8cbfca2c858716b85
- Size of remote file:
- 503 MB
- SHA256:
- e6ecf6f37465e7e3acde5bfba315d8b2a6fd7d443a10935fdeb92f3158305f9e
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