nyu-mll/glue
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How to use Hartunka/distilbert_km_5_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_5_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_5_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_5_v2_cola")This model is a fine-tuned version of Hartunka/distilbert_km_5_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 |
|---|---|---|---|---|---|
| 0.6134 | 1.0 | 34 | 0.6176 | 0.0656 | 0.6932 |
| 0.5825 | 2.0 | 68 | 0.6216 | 0.0445 | 0.6884 |
| 0.5309 | 3.0 | 102 | 0.6439 | 0.0643 | 0.6721 |
| 0.4729 | 4.0 | 136 | 0.7091 | 0.1182 | 0.6894 |
| 0.413 | 5.0 | 170 | 0.7330 | 0.1260 | 0.6405 |
| 0.3505 | 6.0 | 204 | 0.8545 | 0.0740 | 0.6242 |
Base model
Hartunka/distilbert_km_5_v2