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:
- 00e79011a6c7ac88f78d5f5d33877c8b61144c204183109712037e59efe0d865
- Size of remote file:
- 439 MB
- SHA256:
- a88b45e61d9ff2bd55d4bf92edf8aad6d99e647f77c3b13267fae4037dff44ab
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