Instructions to use globuslabs/ScholarBERT_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use globuslabs/ScholarBERT_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="globuslabs/ScholarBERT_1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("globuslabs/ScholarBERT_1") model = AutoModelForMaskedLM.from_pretrained("globuslabs/ScholarBERT_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9ef2d735984d0a375ede723c2b1838ec42531790ae94bbdce2f680971040766
- Size of remote file:
- 1.42 GB
- SHA256:
- ee67dd58116c9eae46d86436dc5877e2793d1eeb535e5cda13306ee29028f704
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