nyu-mll/glue
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How to use Hartunka/distilbert_km_20_v1_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_20_v1_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_20_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_20_v1_stsb")This model is a fine-tuned version of Hartunka/distilbert_km_20_v1 on the GLUE STSB 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 | Pearson | Spearmanr | Combined Score |
|---|---|---|---|---|---|---|
| 3.0017 | 1.0 | 23 | 2.2981 | 0.2026 | 0.1931 | 0.1979 |
| 1.9351 | 2.0 | 46 | 2.2676 | 0.2316 | 0.2087 | 0.2201 |
| 1.726 | 3.0 | 69 | 2.2609 | 0.2611 | 0.2472 | 0.2541 |
| 1.4643 | 4.0 | 92 | 2.5773 | 0.2676 | 0.2552 | 0.2614 |
| 1.1579 | 5.0 | 115 | 2.4631 | 0.2834 | 0.2801 | 0.2817 |
| 0.8956 | 6.0 | 138 | 2.5973 | 0.2666 | 0.2589 | 0.2627 |
| 0.6537 | 7.0 | 161 | 2.8749 | 0.2534 | 0.2460 | 0.2497 |
| 0.5065 | 8.0 | 184 | 2.6949 | 0.2854 | 0.2859 | 0.2857 |
Base model
Hartunka/distilbert_km_20_v1