Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/robertuito_Pfinal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/robertuito_Pfinal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/robertuito_Pfinal")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/robertuito_Pfinal") model = AutoModelForSequenceClassification.from_pretrained("fredymad/robertuito_Pfinal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76efc8bd5a53881e8d342382acc114e539d4c05b92b50a0949447c2a35111a89
- Size of remote file:
- 435 MB
- SHA256:
- 93bebb21a48de14413599db3ce1fb8b872e195a5f88780bccd573bfefed722ed
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