Instructions to use dvilares/bertinho-gl-small-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dvilares/bertinho-gl-small-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dvilares/bertinho-gl-small-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dvilares/bertinho-gl-small-cased") model = AutoModelForMaskedLM.from_pretrained("dvilares/bertinho-gl-small-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c7d1abdcd476d73caefe5da7d87f3ab37109af4a88ea6ad878e13be9b4555e8b
- Size of remote file:
- 266 MB
- SHA256:
- 3e7ca966ec86ade5af4d2adc76c57d551ad720bdaef0c6be42ad67845735f8b1
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