Instructions to use mgh6/temp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgh6/temp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mgh6/temp")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mgh6/temp") model = AutoModelForMaskedLM.from_pretrained("mgh6/temp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8617f3c262e679e3d408b5668a8fb2fbd7a928568aa5170e019afc5e9637c43
- Size of remote file:
- 3.96 kB
- SHA256:
- 52ba119ac824cf9a6973557fe6742b6076b35ca9661ce8d5fcf5368e894fbdfd
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