nyu-mll/glue
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How to use Saul98lm/Prueba4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Saul98lm/Prueba4") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Saul98lm/Prueba4")
model = AutoModelForSequenceClassification.from_pretrained("Saul98lm/Prueba4")This model is a fine-tuned version of distilroberta-base on the glue dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.0931 | 1.09 | 500 | 1.1624 | 0.8260 | 0.8807 |
| 0.0917 | 2.18 | 1000 | 1.1542 | 0.8407 | 0.8845 |