Text Classification
Transformers
Safetensors
Portuguese
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use Silly-Machine/TuPy-Bert-Large-Multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silly-Machine/TuPy-Bert-Large-Multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silly-Machine/TuPy-Bert-Large-Multilabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silly-Machine/TuPy-Bert-Large-Multilabel") model = AutoModelForSequenceClassification.from_pretrained("Silly-Machine/TuPy-Bert-Large-Multilabel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2e7c86874739d624fb59bb7bfb61d31834009ba952e28ad8b4f3058f3523c0d
- Size of remote file:
- 1.34 GB
- SHA256:
- 229a032aa8ab342b3712393c874acc3c5e96e7635ec0619f3fed7e13476293f2
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