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:
- 5c4cc2b771b9afebd68a95048249dcd0b39007e670fb740490bcdfeb0a90ebb1
- Size of remote file:
- 3.12 kB
- SHA256:
- bf9600b80d46c955d52ca410ab46c429f3d0e29a92e22b976bbe9173c8308d80
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