nyu-mll/glue
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How to use jinghan/deberta-base-finetuned-wnli with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="jinghan/deberta-base-finetuned-wnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jinghan/deberta-base-finetuned-wnli")
model = AutoModelForSequenceClassification.from_pretrained("jinghan/deberta-base-finetuned-wnli")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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 40 | 0.6926 | 0.5634 |
| No log | 2.0 | 80 | 0.6911 | 0.5634 |
| No log | 3.0 | 120 | 0.6903 | 0.5634 |
| No log | 4.0 | 160 | 0.6905 | 0.5634 |
| No log | 5.0 | 200 | 0.6904 | 0.5634 |