nyu-mll/glue
Viewer • Updated • 1.49M • 445k • 524
How to use wonkwonlee/distilbert-base-uncased-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="wonkwonlee/distilbert-base-uncased-finetuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("wonkwonlee/distilbert-base-uncased-finetuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("wonkwonlee/distilbert-base-uncased-finetuned-cola", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased 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 | Matthews Correlation |
|---|---|---|---|---|
| 0.5222 | 1.0 | 535 | 0.5384 | 0.4304 |
| 0.3494 | 2.0 | 1070 | 0.5128 | 0.4975 |
| 0.2381 | 3.0 | 1605 | 0.5263 | 0.5475 |
| 0.1753 | 4.0 | 2140 | 0.7498 | 0.5354 |
| 0.1243 | 5.0 | 2675 | 0.8013 | 0.5414 |
Base model
distilbert/distilbert-base-uncased