nyu-mll/glue
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How to use GCopoulos/deberta-finetuned-answer-polarity-7e with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="GCopoulos/deberta-finetuned-answer-polarity-7e") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-7e")
model = AutoModelForSequenceClassification.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-7e")This model is a fine-tuned version of microsoft/deberta-large 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 |
|---|---|---|---|---|
| 0.4752 | 1.0 | 944 | 0.3648 | 0.9140 |
| 0.5769 | 2.0 | 1888 | 0.3024 | 0.9402 |
| 0.1312 | 3.0 | 2832 | 0.2369 | 0.9585 |