Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use fredymad/roberta_Pfinal_4CLASES_2e-5_16_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fredymad/roberta_Pfinal_4CLASES_2e-5_16_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fredymad/roberta_Pfinal_4CLASES_2e-5_16_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fredymad/roberta_Pfinal_4CLASES_2e-5_16_2") model = AutoModelForSequenceClassification.from_pretrained("fredymad/roberta_Pfinal_4CLASES_2e-5_16_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5ddeca1550c840d665f559f080101a9665f2ea134ff7a935d43c4bbb1a1afba
- Size of remote file:
- 499 MB
- SHA256:
- 09364bcd2b691e1c177aa60d52d0af286b7e88a5b47919373764e639f1a47912
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.