Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/robertuito_4CLASES_Pfinal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/robertuito_4CLASES_Pfinal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/robertuito_4CLASES_Pfinal")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/robertuito_4CLASES_Pfinal") model = AutoModelForSequenceClassification.from_pretrained("fredymad/robertuito_4CLASES_Pfinal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a1dfe879b495e45a439e8f1c68c6c27ceac083d04bfe02401ba2e8d864994bd
- Size of remote file:
- 435 MB
- SHA256:
- 6835aa3f516c5dc98779c5a61f5f575056e615b0f11d2e898a6d3cb1b85326e5
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