nyu-mll/glue
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How to use Hieu-Hien/cola-deberta-v3-large with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hieu-Hien/cola-deberta-v3-large") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hieu-Hien/cola-deberta-v3-large")
model = AutoModelForSequenceClassification.from_pretrained("Hieu-Hien/cola-deberta-v3-large", device_map="auto")This model is a fine-tuned version of microsoft/deberta-v3-large on the GLUE COLA 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 | Matthews Correlation |
|---|---|---|---|---|
| 0.3546 | 1.0 | 535 | 0.3123 | 0.7061 |
| 0.2078 | 2.0 | 1070 | 0.3618 | 0.7311 |
| 0.1313 | 3.0 | 1605 | 0.5145 | 0.7160 |
| 0.087 | 4.0 | 2140 | 0.5819 | 0.7230 |
| 0.0597 | 5.0 | 2675 | 0.6325 | 0.7397 |
| 0.0435 | 6.0 | 3210 | 0.6152 | 0.7332 |
| 0.0268 | 7.0 | 3745 | 0.7296 | 0.7327 |
| 0.0304 | 8.0 | 4280 | 0.7672 | 0.7287 |
| 0.015 | 9.0 | 4815 | 0.8067 | 0.7264 |
| 0.0133 | 10.0 | 5350 | 0.8079 | 0.7246 |
Base model
microsoft/deberta-v3-large