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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 84f62b46b7f0714b03b0c2768d0e2f3a8d0d8f3a0d7f3081ecac09d4f96a5dfa
- Size of remote file:
- 675 MB
- SHA256:
- b1ea5acbafaf8e058e92b56080e7199ffbd7b19fdfaa1787b59c308c27407f0e
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