Instructions to use JFrediani/Bertimbau-Large-Offensive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JFrediani/Bertimbau-Large-Offensive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JFrediani/Bertimbau-Large-Offensive")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JFrediani/Bertimbau-Large-Offensive") model = AutoModelForSequenceClassification.from_pretrained("JFrediani/Bertimbau-Large-Offensive", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec5764b02817dd9fc6d5f263db74af6b38f5f1923f7268400da5ca2d1086b5ef
- Size of remote file:
- 1.34 GB
- SHA256:
- 908caa5a00c8e064bb325d15e6ae4d667e4be9939a31988888dfc67c1980cbb4
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