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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7daebecb6b0907ed6c11b7366d1b57c52442f583cc9b4fdafe46d765e0a2dbe3
- Size of remote file:
- 695 MB
- SHA256:
- 6bbb0676f967fbd29fa846bb8526d30b0a6a8392b0b697e747f766adec18b2b1
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