nyu-mll/glue
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How to use GCopoulos/deberta-finetuned-answer-polarity-5e with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="GCopoulos/deberta-finetuned-answer-polarity-5e") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-5e")
model = AutoModelForSequenceClassification.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-5e")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 | F1 |
|---|---|---|---|---|
| No log | 1.0 | 364 | 0.5350 | 0.8208 |
| 0.1301 | 2.0 | 728 | 0.7435 | 0.7378 |
| 0.1716 | 3.0 | 1092 | 0.4829 | 0.8193 |
| 0.1716 | 4.0 | 1456 | 0.5184 | 0.8124 |
| 0.1455 | 5.0 | 1820 | 0.5116 | 0.8572 |