Instructions to use jgammack/SAE-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jgammack/SAE-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jgammack/SAE-bert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jgammack/SAE-bert-base-uncased") model = AutoModelForMaskedLM.from_pretrained("jgammack/SAE-bert-base-uncased") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#3 opened over 1 year ago
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SFconvertbot
Librarian Bot: Add base_model information to model
#2 opened almost 3 years ago
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librarian-bot
Librarian Bot: Update dataset YAML metadata for model
#1 opened over 3 years ago
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librarian-bot