Instructions to use belisards/congretimbau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use belisards/congretimbau with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="belisards/congretimbau")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("belisards/congretimbau") model = AutoModelForMaskedLM.from_pretrained("belisards/congretimbau", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96693f82f69c1f09891317741713fab76a3ead5e6f2850482cd314a353ccce12
- Size of remote file:
- 5.3 kB
- SHA256:
- f9841ec8c9e518bc0b65fecbad3a31d0af2626220d3b60f784bc99c24535021a
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