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:
- 1707c4e9e19709f3f259be5a303d1add3dad3215072de6545dd18ad7360a24a9
- Size of remote file:
- 856 MB
- SHA256:
- 817115e02a57350e7e1681adf6d8cfd025b31513900bbb32a0bd3fa59df696d6
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