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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dac07d68aeb605576763e293c6bbb287c56a1c1c8cfc5a8cef13848134da3d7a
- Size of remote file:
- 777 MB
- SHA256:
- 8bc177f72b316ca4337525505360b7868cbcbae5f711478c8e2553445d65cd21
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