nyu-mll/glue
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How to use Jcfranco/distilbert-base-uncased-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Jcfranco/distilbert-base-uncased-finetuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Jcfranco/distilbert-base-uncased-finetuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("Jcfranco/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:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| No log | 1.0 | 54 | 0.5807 | 0.0 |
| No log | 2.0 | 108 | 0.6416 | 0.2717 |
| No log | 3.0 | 162 | 0.7327 | 0.3380 |
| No log | 4.0 | 216 | 0.9711 | 0.3521 |
| No log | 5.0 | 270 | 1.0426 | 0.3558 |