Instructions to use globuslabs/ScholarBERT_100_64bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use globuslabs/ScholarBERT_100_64bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="globuslabs/ScholarBERT_100_64bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("globuslabs/ScholarBERT_100_64bit") model = AutoModelForMaskedLM.from_pretrained("globuslabs/ScholarBERT_100_64bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6adb5ecc3bfd8a3b9073274250808f4e9011551544b99a1c722b5840d5e9dbe7
- Size of remote file:
- 1.42 GB
- SHA256:
- 607293e37a589a30922915d0071920411a2f6f5c184e1d5258d425e4b76e4074
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