Instructions to use Flaglab/SciBETO-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Flaglab/SciBETO-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Flaglab/SciBETO-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Flaglab/SciBETO-large") model = AutoModelForMaskedLM.from_pretrained("Flaglab/SciBETO-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d25512f742873dc8d0699b9264c258761764aec356001c5e3c0d5792efd07ee
- Size of remote file:
- 1.42 GB
- SHA256:
- c451bf3cf0742547b7a56c1c555d3f2c792a1368be19bb86155423d0a7baa530
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