nyu-mll/glue
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How to use Hartunka/distilbert_km_50_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_50_v1_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_50_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_50_v1_cola")This model is a fine-tuned version of Hartunka/distilbert_km_50_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 |
|---|---|---|---|---|---|
| 0.6173 | 1.0 | 34 | 0.6217 | 0.0 | 0.6913 |
| 0.6 | 2.0 | 68 | 0.6171 | 0.0416 | 0.6922 |
| 0.567 | 3.0 | 102 | 0.6260 | 0.1112 | 0.6932 |
| 0.5196 | 4.0 | 136 | 0.6526 | 0.1044 | 0.6865 |
| 0.4429 | 5.0 | 170 | 0.6837 | 0.1504 | 0.6606 |
| 0.3773 | 6.0 | 204 | 0.7564 | 0.1223 | 0.6558 |
| 0.31 | 7.0 | 238 | 0.8318 | 0.1250 | 0.6453 |
Base model
Hartunka/distilbert_km_50_v1