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:
- 413b4d2e8fb21f2dbec6ae8bef86d179e5312692f4ad835acd13b71e24523a9f
- Size of remote file:
- 692 MB
- SHA256:
- a874bd08278dc69299304236d668463e405b4f8b311c1ff10e2dfeba6b654cbc
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