nyu-mll/glue
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How to use Hartunka/distilbert_km_50_v1_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_50_v1_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_50_v1_rte")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_50_v1_rte")This model is a fine-tuned version of Hartunka/distilbert_km_50_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 |
|---|---|---|---|---|
| 0.7041 | 1.0 | 10 | 0.7229 | 0.4874 |
| 0.6699 | 2.0 | 20 | 0.7101 | 0.4874 |
| 0.6271 | 3.0 | 30 | 0.7358 | 0.4910 |
| 0.5738 | 4.0 | 40 | 0.7641 | 0.4910 |
| 0.4898 | 5.0 | 50 | 0.8819 | 0.5090 |
| 0.388 | 6.0 | 60 | 1.0098 | 0.4765 |
| 0.2661 | 7.0 | 70 | 1.1909 | 0.4693 |
Base model
Hartunka/distilbert_km_50_v1