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:
- 9f2266293616a0966dfc1298f65b909264afcf4b37bb7eea33bdd72fad25074a
- Size of remote file:
- 14.6 kB
- SHA256:
- 21e6043623154d9ca0eaed6622eb9b16fccb00c353b81622b878bc92b640b36e
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