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:
- ec7b92c2522ccf75cae39f2215eade841f28ddd40a39b327021be6f0a648f684
- Size of remote file:
- 660 MB
- SHA256:
- c67c8e3e535023fdb624a5f62793ebe08f9b7cdebe085bb8ad8222ba7491efa4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.