Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Dulfary/platzi-distilroberta-base-mrpc-glue-prueba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dulfary/platzi-distilroberta-base-mrpc-glue-prueba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dulfary/platzi-distilroberta-base-mrpc-glue-prueba")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dulfary/platzi-distilroberta-base-mrpc-glue-prueba") model = AutoModelForSequenceClassification.from_pretrained("Dulfary/platzi-distilroberta-base-mrpc-glue-prueba", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.8235294117647058, | |
| "eval_f1": 0.8783783783783784, | |
| "eval_loss": 0.7472043037414551, | |
| "eval_runtime": 58.9814, | |
| "eval_samples_per_second": 6.917, | |
| "eval_steps_per_second": 0.865, | |
| "total_flos": 205828464972624.0, | |
| "train_loss": 0.2136054527266093, | |
| "train_runtime": 5995.4335, | |
| "train_samples_per_second": 1.835, | |
| "train_steps_per_second": 0.23 | |
| } |