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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kenkaneki/bert-base-aeslc") model = AutoModelForMaskedLM.from_pretrained("kenkaneki/bert-base-aeslc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfd35299da5bf3e467cdb6cf20ef45e9132072e601cd52a358fcbe6bd96848f4
- Size of remote file:
- 438 MB
- SHA256:
- 257d826095e309b6cba7372e2f02c51062fb5e48833adea53bf48670f0ed7fb1
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