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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9933e0138a450212cd1e82789a0bfe97a1ea0c6bcecded5515502a00ac0ff3e3
- Size of remote file:
- 1.43 kB
- SHA256:
- 637bf06fac5867cbb09f2dd2269c5637d9d7080f340955a68602b01fe029db15
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