nyu-mll/glue
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How to use Hartunka/distilbert_km_10_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_10_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_10_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_10_v2_cola")This model is a fine-tuned version of Hartunka/distilbert_km_10_v2 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 | Accuracy |
|---|---|---|---|---|---|
| 0.6152 | 1.0 | 34 | 0.6164 | 0.0464 | 0.6922 |
| 0.5852 | 2.0 | 68 | 0.6236 | 0.0353 | 0.6817 |
| 0.5375 | 3.0 | 102 | 0.6428 | 0.0134 | 0.6491 |
| 0.4823 | 4.0 | 136 | 0.6820 | 0.0899 | 0.6548 |
| 0.4156 | 5.0 | 170 | 0.7540 | 0.0517 | 0.6270 |
| 0.3536 | 6.0 | 204 | 0.8315 | 0.0823 | 0.6491 |
Base model
Hartunka/distilbert_km_10_v2