Instructions to use dvilares/bertinho-gl-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dvilares/bertinho-gl-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dvilares/bertinho-gl-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dvilares/bertinho-gl-base-cased") model = AutoModelForMaskedLM.from_pretrained("dvilares/bertinho-gl-base-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7add362f8351bd6cf8809c477844c53bb5d1792d72919406ed76b1f2b389cab5
- Size of remote file:
- 436 MB
- SHA256:
- a475bb087f242bbbc2581ffae7346e0dd2dfcacbbc739fb617b6cd57c344e3ef
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