nyu-mll/glue
Viewer • Updated • 1.49M • 485k • 498
How to use GCopoulos/deberta-finetuned-answer-polarity-1e6 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="GCopoulos/deberta-finetuned-answer-polarity-1e6") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-1e6")
model = AutoModelForSequenceClassification.from_pretrained("GCopoulos/deberta-finetuned-answer-polarity-1e6")This model is a fine-tuned version of microsoft/deberta-large on the glue dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 262 | 0.7424 | 0.4877 |
| 0.8987 | 2.0 | 524 | 0.3792 | 0.8774 |
| 0.2993 | 3.0 | 786 | 0.5936 | 0.8413 |
| 0.1483 | 4.0 | 1048 | 0.4211 | 0.8859 |
| 0.1175 | 5.0 | 1310 | 0.4684 | 0.8959 |
| 0.0816 | 6.0 | 1572 | 0.6284 | 0.8712 |
| 0.0624 | 7.0 | 1834 | 0.7823 | 0.8586 |