Instructions to use pstroe/roberta-base-latin-cased3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pstroe/roberta-base-latin-cased3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pstroe/roberta-base-latin-cased3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pstroe/roberta-base-latin-cased3") model = AutoModelForMaskedLM.from_pretrained("pstroe/roberta-base-latin-cased3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a1aa5aa8f19a1eb873c3dd8cff087e607c677efe55f850014d6a69ea33bae18
- Size of remote file:
- 445 MB
- SHA256:
- 295c02f182a33bed590a65dfa74e2f53b96a3ea04c8800496d9195954782f99c
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