Instructions to use batterydata/batterybert-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use batterydata/batterybert-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="batterydata/batterybert-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("batterydata/batterybert-cased") model = AutoModelForMaskedLM.from_pretrained("batterydata/batterybert-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7351e8d1062e1a73731f5336ec43ab9c2715cfd01382ee70ae18dadfa2352c99
- Size of remote file:
- 433 MB
- SHA256:
- 791bfd71b1a39b154f98333540b00ee8c6eddd10445261bc84ec8c3ba2fc9219
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.