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:
- 010610b2374cec3684691470f4c7ff1b0c1a30e88fa204389b9fe2a57292b259
- Size of remote file:
- 706 MB
- SHA256:
- 44cf531a4c455d3e2dc731031873378b812de20c788bb31f37f4da865d243091
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