Instructions to use bencyc1129/bert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bencyc1129/bert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bencyc1129/bert-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bencyc1129/bert-small") model = AutoModelForMaskedLM.from_pretrained("bencyc1129/bert-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 593db4b3699374ffe62ee51c94c5d6d681743def4025deeb7391484719941e26
- Size of remote file:
- 258 MB
- SHA256:
- ece31f1a9fa6fd332a511e19fb05fef4072e7484972a9568f13c8d0acb1db83c
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