nyu-mll/glue
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How to use Hartunka/distilbert_km_100_v1_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_100_v1_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_100_v1_rte")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_100_v1_rte")This model is a fine-tuned version of Hartunka/distilbert_km_100_v1 on the GLUE RTE 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 | Accuracy |
|---|---|---|---|---|
| 0.7053 | 1.0 | 10 | 0.7318 | 0.4404 |
| 0.6741 | 2.0 | 20 | 0.7099 | 0.5018 |
| 0.6343 | 3.0 | 30 | 0.7236 | 0.5307 |
| 0.5864 | 4.0 | 40 | 0.7435 | 0.5343 |
| 0.5129 | 5.0 | 50 | 0.8149 | 0.5451 |
| 0.4216 | 6.0 | 60 | 0.9012 | 0.5018 |
| 0.2957 | 7.0 | 70 | 1.1130 | 0.5271 |
Base model
Hartunka/distilbert_km_100_v1