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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bdd0b8d5c972576771dcaa852760501df0087d725bd0b0e2ba06fc9ab22aba8
- Size of remote file:
- 499 MB
- SHA256:
- fdbc9687f13e1bc58cb47366d844026a7e76ab32f18a69565571680ffc5daadd
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