Instructions to use ExponentialScience/LedgerBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ExponentialScience/LedgerBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ExponentialScience/LedgerBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ExponentialScience/LedgerBERT") model = AutoModelForMaskedLM.from_pretrained("ExponentialScience/LedgerBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed7efa5cd0402574e3015fd2928afe45d3aa2978ac0d539b55fdf164aea905c2
- Size of remote file:
- 5.84 kB
- SHA256:
- dc1073b7dce21911860af0ef49fed0ddc5b4e2f02e488fdd327d7dc3e1dd40d1
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