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
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Download sample_data/README.md from JFrediani/Bertimbau-Large-Offensive: direct link, hf CLI and curl.
- Browser
- Download file 930 Bytes
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https://huggingface.co/JFrediani/Bertimbau-Large-Offensive/resolve/main/sample_data/README.md
- Command line
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hf download hf://JFrediani/Bertimbau-Large-Offensive/sample_data/README.md
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curl -L -o README.md https://huggingface.co/JFrediani/Bertimbau-Large-Offensive/resolve/main/sample_data/README.md
930 Bytes
This directory includes a few sample datasets to get you started.
california_housing_data*.csvis California housing data from the 1990 US Census; more information is available at: https://developers.google.com/machine-learning/crash-course/california-housing-data-descriptionmnist_*.csvis a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/anscombe.jsoncontains a copy of Anscombe's quartet; it was originally described inAnscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the vega_datasets library.