nyu-mll/glue
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How to use Hartunka/distilbert_km_50_v2_cola with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_50_v2_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_50_v2_cola")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_50_v2_cola")This model is a fine-tuned version of Hartunka/distilbert_km_50_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 |
|---|---|---|---|---|---|
| 0.6213 | 1.0 | 34 | 0.6252 | 0.0 | 0.6913 |
| 0.6057 | 2.0 | 68 | 0.6239 | 0.0 | 0.6913 |
| 0.5869 | 3.0 | 102 | 0.6300 | 0.0174 | 0.6846 |
| 0.5567 | 4.0 | 136 | 0.6485 | 0.0735 | 0.6692 |
| 0.5056 | 5.0 | 170 | 0.6888 | 0.0555 | 0.6577 |
| 0.4515 | 6.0 | 204 | 0.7535 | 0.0115 | 0.6414 |
| 0.3961 | 7.0 | 238 | 0.8222 | 0.0215 | 0.6366 |
Base model
Hartunka/distilbert_km_50_v2