Instructions to use vertigoq3/modeloEmailLabels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vertigoq3/modeloEmailLabels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vertigoq3/modeloEmailLabels")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vertigoq3/modeloEmailLabels") model = AutoModelForSequenceClassification.from_pretrained("vertigoq3/modeloEmailLabels") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 746072322573e182237850341bab847ffeec998d1aa2007de0419e345ef5c1cd
- Size of remote file:
- 711 MB
- SHA256:
- 448cd46741fca43fd698d7a1aeee4bbfb17963e279ef434526f680f7207e3f62
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