nyu-mll/glue
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How to use Hartunka/distilbert_rand_5_v1_rte with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_rand_5_v1_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_rand_5_v1_rte")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_rand_5_v1_rte")This model is a fine-tuned version of Hartunka/distilbert_rand_5_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 |
|---|---|---|---|---|
| 0.7243 | 1.0 | 10 | 0.7026 | 0.4874 |
| 0.6823 | 2.0 | 20 | 0.7102 | 0.5235 |
| 0.6402 | 3.0 | 30 | 0.7390 | 0.5668 |
| 0.5556 | 4.0 | 40 | 0.8524 | 0.5307 |
| 0.439 | 5.0 | 50 | 1.0052 | 0.5235 |
| 0.2973 | 6.0 | 60 | 1.2635 | 0.5235 |
Base model
Hartunka/distilbert_rand_5_v1