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:
- f7d11d9681246f64d1c9d51125297ed1f87cad3da7324d269471a6ec61bbc27c
- Size of remote file:
- 1.47 kB
- SHA256:
- 0dc0ad69b77141cb0303abdfc700889658518444ae0177a1944665ea3cd5f2d3
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