nyu-mll/glue
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How to use GCopoulos/deberta-finetuned-answer-polarity-3e6-newdata3 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="GCopoulos/deberta-finetuned-answer-polarity-3e6-newdata3") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-3e6-newdata3")
model = AutoModelForSequenceClassification.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-3e6-newdata3")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 | 219 | 0.4594 | 0.8532 |
| 0.5223 | 2.0 | 438 | 0.5479 | 0.8841 |
| 0.0962 | 3.0 | 657 | 0.7485 | 0.8848 |