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:
- 59a53c7403eddca5ee51a7670f8dd8f494876c944cb6667febf28a6c837348a3
- Size of remote file:
- 129 MB
- SHA256:
- e3331ed6282bcd63c6702b31bad0121b58ab2a878a5a7749e9bdd41fbff65da3
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