nyu-mll/glue
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How to use Hartunka/distilbert_km_10_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_10_v1_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_10_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_10_v1_cola")This model is a fine-tuned version of Hartunka/distilbert_km_10_v1 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 |
|---|---|---|---|---|---|
| 0.6167 | 1.0 | 34 | 0.6198 | 0.0656 | 0.6932 |
| 0.5902 | 2.0 | 68 | 0.6214 | 0.0584 | 0.6874 |
| 0.5443 | 3.0 | 102 | 0.6365 | 0.0209 | 0.6654 |
| 0.4812 | 4.0 | 136 | 0.6850 | 0.0702 | 0.6711 |
| 0.4113 | 5.0 | 170 | 0.7599 | 0.0616 | 0.6318 |
| 0.3473 | 6.0 | 204 | 0.8575 | 0.0842 | 0.6510 |
Base model
Hartunka/distilbert_km_10_v1