Instructions to use tubyneto/bertimbau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tubyneto/bertimbau with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="tubyneto/bertimbau")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tubyneto/bertimbau") model = AutoModelForMaskedLM.from_pretrained("tubyneto/bertimbau") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d7a6766ef146768ed57ba8528b9f63474b27151f6ba386ad459736f13715783
- Size of remote file:
- 436 MB
- SHA256:
- 96d2144445b6ba3530c27e38e7e27139fd0b0a5e36d9ca66f4155da7c5f199b0
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