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")# pip install -U transformers accelerate # 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
Download sample_data/mnist_test.csv from JFrediani/Bertimbau-Large-Offensive: direct link, hf CLI and curl.
- Browser
- Download file 18.3 MB
-
https://huggingface.co/JFrediani/Bertimbau-Large-Offensive/resolve/main/sample_data/mnist_test.csv
- Command line
-
hf download hf://JFrediani/Bertimbau-Large-Offensive/sample_data/mnist_test.csv
-
curl -L -o mnist_test.csv https://huggingface.co/JFrediani/Bertimbau-Large-Offensive/resolve/main/sample_data/mnist_test.csv
18.3 MB
- Xet hash:
- 4c3587f03c90386f5c109436735436a62788da4696285b74c1bcab09d64d103f
- Size of remote file:
- 18.3 MB
- SHA256:
- 51c292478d94ec3a01461bdfa82eb0885d262eb09e615679b2d69dedb6ad09e7
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