Instructions to use nairaxo/toumbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nairaxo/toumbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nairaxo/toumbert", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nairaxo/toumbert") model = AutoModelForMaskedLM.from_pretrained("nairaxo/toumbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 040ab7b2606fef71b73bbc1da37f2222fc3fd2b06e915272a5c9f339f10d1149
- Size of remote file:
- 808 MB
- SHA256:
- b11a4aa6f8933fddb102ee947047ba420b9b4a2e0bb987d4518bfdf5ac2daa32
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