Text Classification
Transformers
Joblib
Portuguese
streamlit
multi-label-classification
gradient-boosting
active-learning
bertimbau
municipal-documents
meeting-minutes
Instructions to use anonymous12321/Council_Topics_Classifier_PT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anonymous12321/Council_Topics_Classifier_PT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anonymous12321/Council_Topics_Classifier_PT")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anonymous12321/Council_Topics_Classifier_PT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download gradient_boosting_logistic_model.joblib from anonymous12321/Council_Topics_Classifier_PT: direct link, hf CLI and curl.
- Browser
- Download file 1.91 MB
-
https://huggingface.co/anonymous12321/Council_Topics_Classifier_PT/resolve/main/gradient_boosting_logistic_model.joblib
- Command line
-
hf download hf://anonymous12321/Council_Topics_Classifier_PT/gradient_boosting_logistic_model.joblib
-
curl -L -o gradient_boosting_logistic_model.joblib https://huggingface.co/anonymous12321/Council_Topics_Classifier_PT/resolve/main/gradient_boosting_logistic_model.joblib
1.91 MB
- Xet hash:
- 892b98027ca81cb2dbe42374f9c3a54280e47283c32b9dda928818ffe8b51498
- Size of remote file:
- 1.91 MB
- SHA256:
- d8ca35ecf58dbf92d27ae4f23ae42dedcf468299eb4f7e012e89b73b9c3c4660
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