Instructions to use baikalai/dbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baikalai/dbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="baikalai/dbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("baikalai/dbert") model = AutoModelForMaskedLM.from_pretrained("baikalai/dbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from baikalai/dbert: direct link, hf CLI and curl.
- Browser
- Download file 133 Bytes
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https://huggingface.co/baikalai/dbert/resolve/main/README.md
- Command line
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hf download hf://baikalai/dbert/README.md
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curl -L -o README.md https://huggingface.co/baikalai/dbert/resolve/main/README.md
133 Bytes
metadata
language: ko
datasets:
- kowiki
- news
deeqBERT-base
- model: bert-base
- vocab: bert-wordpiece, 35k
- version: latest