Text Classification
Transformers
Safetensors
Portuguese
bert
Eval Results (legacy)
text-embeddings-inference
Instructions to use Silly-Machine/TuPy-Bert-Base-Multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Silly-Machine/TuPy-Bert-Base-Multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Silly-Machine/TuPy-Bert-Base-Multilabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Silly-Machine/TuPy-Bert-Base-Multilabel") model = AutoModelForSequenceClassification.from_pretrained("Silly-Machine/TuPy-Bert-Base-Multilabel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a474ab3295a02985a94c09aac25231a55c06031e5850d1c3e16bd60ab8dd2fb
- Size of remote file:
- 436 MB
- SHA256:
- c5ff9906df7f56742f5b71af51e0e30ec8034921893729544b4560a2a2eb294e
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