nyu-mll/glue
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How to use autoevaluate/glue-qqp with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="autoevaluate/glue-qqp") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("autoevaluate/glue-qqp")
model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/glue-qqp")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 | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2779 | 1.0 | 22741 | 0.2697 | 0.8871 | 0.8494 |
| 0.2183 | 2.0 | 45482 | 0.2651 | 0.8966 | 0.8634 |
| 0.1635 | 3.0 | 68223 | 0.3116 | 0.9013 | 0.8685 |
| 0.1312 | 4.0 | 90964 | 0.4102 | 0.9030 | 0.8694 |
| 0.0802 | 5.0 | 113705 | 0.4798 | 0.9033 | 0.8703 |