Instructions to use kenkaneki/bert-base-aeslc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenkaneki/bert-base-aeslc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kenkaneki/bert-base-aeslc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kenkaneki/bert-base-aeslc") model = AutoModelForMaskedLM.from_pretrained("kenkaneki/bert-base-aeslc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
Verified signature- Adding `safetensors` variant of this model (aa14502d05f84c73888b7b65368af5897dc42195) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>4c66afd
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add tokenizer
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add model
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initial commit
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