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:
- a24c2bf24a993cde233ac36cd159905e85d2a52201fac6d4ee4527ca0dd26287
- Size of remote file:
- 3.64 kB
- SHA256:
- 69d16ef1e14dbf7052b8eedae364e49fe6704f4729c986ea7296d758d5c5d593
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.