nyu-mll/glue
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How to use JOSEDURANisc/practicaNLP with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="JOSEDURANisc/practicaNLP") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("JOSEDURANisc/practicaNLP")
model = AutoModelForSequenceClassification.from_pretrained("JOSEDURANisc/practicaNLP")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 |
|---|---|---|---|---|---|
| 0.4012 | 1.09 | 500 | 0.5515 | 0.8235 | 0.8763 |
| 0.3039 | 2.18 | 1000 | 0.7396 | 0.8358 | 0.8847 |
Base model
distilbert/distilroberta-base