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:
- f5247a97222fd43fa633174a397d04c73ed696c470d78810bade86719d4449a7
- Size of remote file:
- 101 MB
- SHA256:
- 44a9c6f3e91d06615b6330f100bfc6900a6443e5ca5020ffe647c3d2b83001e3
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