nyu-mll/glue
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How to use GCopoulos/debertav3-finetuned-answer-polarity-2e6-newdata with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="GCopoulos/debertav3-finetuned-answer-polarity-2e6-newdata") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GCopoulos/debertav3-finetuned-answer-polarity-2e6-newdata")
model = AutoModelForSequenceClassification.from_pretrained("GCopoulos/debertav3-finetuned-answer-polarity-2e6-newdata")This model is a fine-tuned version of microsoft/deberta-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 | F1 |
|---|---|---|---|---|
| No log | 1.0 | 221 | 1.1820 | 0.2111 |
| 1.1195 | 2.0 | 442 | 0.7073 | 0.7068 |
| 0.4953 | 3.0 | 663 | 0.5068 | 0.8311 |
| 0.4953 | 4.0 | 884 | 0.4326 | 0.8498 |
| 0.2767 | 5.0 | 1105 | 0.4155 | 0.8553 |
| 0.2147 | 6.0 | 1326 | 0.4009 | 0.8527 |