Instructions to use olm/olm-roberta-base-dec-2022 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olm/olm-roberta-base-dec-2022 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="olm/olm-roberta-base-dec-2022")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("olm/olm-roberta-base-dec-2022") model = AutoModelForMaskedLM.from_pretrained("olm/olm-roberta-base-dec-2022", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e142a2c6ff30e9a34cceb649ccc70c807494a725a531aabeb2fbb06cdd7494d
- Size of remote file:
- 499 MB
- SHA256:
- 6d4a6603680a1cbfc2d21ff0e20cb2932be649e5db2cddf8172c9b777a5f247e
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