nyu-mll/glue
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How to use RazyDave/deberta-v3-base-finetuned-rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="RazyDave/deberta-v3-base-finetuned-rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("RazyDave/deberta-v3-base-finetuned-rte")
model = AutoModelForSequenceClassification.from_pretrained("RazyDave/deberta-v3-base-finetuned-rte")This model is a fine-tuned version of microsoft/deberta-v3-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 |
|---|---|---|---|---|
| No log | 1.0 | 156 | 0.5610 | 0.7545 |
| No log | 2.0 | 312 | 0.6270 | 0.7617 |
| No log | 3.0 | 468 | 0.6565 | 0.7906 |
| 0.3919 | 4.0 | 624 | 0.8234 | 0.8195 |
| 0.3919 | 5.0 | 780 | 0.9628 | 0.7978 |