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:
- fb9bd4d06e13eb91ebaaf2ed4c84770b7783352ce2e91c371e28532ed7d1864a
- Size of remote file:
- 678 MB
- SHA256:
- 7615a472fb1a3ccc378c6fa8e0c468fb4d5eae2b8c335540cdc10eda7b97929c
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