Instructions to use vasugoel/K-12BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vasugoel/K-12BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vasugoel/K-12BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vasugoel/K-12BERT") model = AutoModelForMaskedLM.from_pretrained("vasugoel/K-12BERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ae2e96556324d29ca51f1eb4d4324c9d7c8197209624287eb34e502fb902104
- Size of remote file:
- 2.86 kB
- SHA256:
- c5f37a12d7e6a968fd62fdf72dabb211f779bb02d75490b9ddbc8c48073d63cc
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