nyu-mll/glue
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How to use Hartunka/distilbert_km_10_v1_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Hartunka/distilbert_km_10_v1_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/distilbert_km_10_v1_stsb")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/distilbert_km_10_v1_stsb")This model is a fine-tuned version of Hartunka/distilbert_km_10_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 |
|---|---|---|---|---|---|---|
| 2.9054 | 1.0 | 23 | 2.2226 | 0.1878 | 0.1731 | 0.1804 |
| 1.9443 | 2.0 | 46 | 2.3643 | 0.2239 | 0.2038 | 0.2139 |
| 1.6962 | 3.0 | 69 | 2.3195 | 0.2373 | 0.2300 | 0.2337 |
| 1.3248 | 4.0 | 92 | 2.2913 | 0.2778 | 0.2787 | 0.2783 |
| 0.9649 | 5.0 | 115 | 2.6289 | 0.2679 | 0.2579 | 0.2629 |
| 0.6945 | 6.0 | 138 | 2.6541 | 0.2831 | 0.2754 | 0.2793 |
Base model
Hartunka/distilbert_km_10_v1