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:
- ed882cbeafca8b0a80672db1b55f890c7b3d54be4d2b6920da334116d50f04f8
- Size of remote file:
- 220 MB
- SHA256:
- e03a99276407e91f775f442b3ac9eff90468631540a3242ac9d4073c28368cad
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