nyu-mll/glue
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How to use Hartunka/distilbert_km_5_v1_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_5_v1_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_5_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_5_v1_stsb")This model is a fine-tuned version of Hartunka/distilbert_km_5_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 |
|---|---|---|---|---|---|---|
| 2.8255 | 1.0 | 23 | 2.2348 | 0.2162 | 0.1982 | 0.2072 |
| 1.9258 | 2.0 | 46 | 2.5031 | 0.2047 | 0.1904 | 0.1976 |
| 1.6928 | 3.0 | 69 | 2.2661 | 0.2879 | 0.2819 | 0.2849 |
| 1.3472 | 4.0 | 92 | 2.1893 | 0.3284 | 0.3228 | 0.3256 |
| 0.9832 | 5.0 | 115 | 2.3848 | 0.3163 | 0.3160 | 0.3162 |
| 0.7173 | 6.0 | 138 | 2.4738 | 0.3161 | 0.3113 | 0.3137 |
| 0.5115 | 7.0 | 161 | 2.7082 | 0.3147 | 0.3100 | 0.3123 |
| 0.4004 | 8.0 | 184 | 2.4274 | 0.3546 | 0.3492 | 0.3519 |
| 0.3309 | 9.0 | 207 | 2.5825 | 0.3325 | 0.3244 | 0.3284 |
Base model
Hartunka/distilbert_km_5_v1