Instructions to use PoetschLab/GROVER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PoetschLab/GROVER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="PoetschLab/GROVER")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("PoetschLab/GROVER") model = AutoModelForMaskedLM.from_pretrained("PoetschLab/GROVER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 586a9fef98df87fe9d04a43058424b45a9835473042377fddb2e7bd69d2a6039
- Size of remote file:
- 131 MB
- SHA256:
- c82b79457e05808c1f4fd655e72abf7921ae094e7bd7763ac57f2dfd7637338c
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