nyu-mll/glue
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How to use Hartunka/distilbert_km_100_v1_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_100_v1_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_100_v1_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_100_v1_cola")This model is a fine-tuned version of Hartunka/distilbert_km_100_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 |
|---|---|---|---|---|---|
| 0.6195 | 1.0 | 34 | 0.6249 | 0.0 | 0.6913 |
| 0.601 | 2.0 | 68 | 0.6169 | 0.0416 | 0.6922 |
| 0.5701 | 3.0 | 102 | 0.6249 | 0.0117 | 0.6759 |
| 0.5237 | 4.0 | 136 | 0.6790 | 0.0385 | 0.6683 |
| 0.4548 | 5.0 | 170 | 0.6883 | 0.0972 | 0.6481 |
| 0.3985 | 6.0 | 204 | 0.7564 | 0.0769 | 0.6453 |
| 0.3411 | 7.0 | 238 | 0.8330 | 0.0623 | 0.6242 |
Base model
Hartunka/distilbert_km_100_v1