nyu-mll/glue
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How to use jpabbuehl/distilbert-base-uncased-finetuned-cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="jpabbuehl/distilbert-base-uncased-finetuned-cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jpabbuehl/distilbert-base-uncased-finetuned-cola")
model = AutoModelForSequenceClassification.from_pretrained("jpabbuehl/distilbert-base-uncased-finetuned-cola")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 |
|---|---|---|---|---|
| 0.5261 | 1.0 | 535 | 0.5125 | 0.4124 |
| 0.3502 | 2.0 | 1070 | 0.5439 | 0.5076 |
| 0.2378 | 3.0 | 1605 | 0.6629 | 0.4946 |
| 0.1809 | 4.0 | 2140 | 0.7588 | 0.5230 |
| 0.1309 | 5.0 | 2675 | 0.8901 | 0.5056 |