Instructions to use rdenadai/BR_BERTo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdenadai/BR_BERTo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rdenadai/BR_BERTo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rdenadai/BR_BERTo") model = AutoModelForMaskedLM.from_pretrained("rdenadai/BR_BERTo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6c2f689803acf99d5840a995e7faa20290683312999649a7829eb9535c27108
- Size of remote file:
- 1.46 kB
- SHA256:
- cb78a6009462360d60fccfba5593ec9de00488005407820e0741097a75e978eb
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