Instructions to use Flaglab/SciBETO-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Flaglab/SciBETO-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Flaglab/SciBETO-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Flaglab/SciBETO-base") model = AutoModelForMaskedLM.from_pretrained("Flaglab/SciBETO-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93a339dc9c3f6031e90961cd86af5ab48fa891cdbedd501c3279d8c794661588
- Size of remote file:
- 499 MB
- SHA256:
- eaa5823e8aa13dec51f0c92f643e8300f1e01d12b994101c52ce857956bfbcc8
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