Instructions to use globuslabs/ScholarBERT_100_WB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use globuslabs/ScholarBERT_100_WB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="globuslabs/ScholarBERT_100_WB")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("globuslabs/ScholarBERT_100_WB") model = AutoModelForMaskedLM.from_pretrained("globuslabs/ScholarBERT_100_WB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e7032b4066df86eec57cb1dea66f8a9f1ec2b5fae9d0a691bf767d5477f8a9b7
- Size of remote file:
- 1.42 GB
- SHA256:
- 37321f47d79322692cfb88d3c659bc585c92aecd7fb032e4c28d5b2e9ceb30dd
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