nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb")This model is a fine-tuned version of google/mobilebert-uncased 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 |
|---|---|---|---|---|---|---|
| 1.7465 | 1.0 | 45 | 1.2026 | 0.0588 | 0.0666 | 0.0627 |
| 1.079 | 2.0 | 90 | 1.4599 | 0.0595 | 0.0691 | 0.0643 |
| 1.0784 | 3.0 | 135 | 1.2063 | 0.0611 | 0.0707 | 0.0659 |
| 0.9943 | 4.0 | 180 | 1.3534 | 0.0730 | 0.0730 | 0.0730 |
| 0.9523 | 5.0 | 225 | 1.3943 | 0.1080 | 0.1010 | 0.1045 |
| 0.8379 | 6.0 | 270 | 1.1918 | 0.1864 | 0.1859 | 0.1862 |
| 0.7217 | 7.0 | 315 | 1.2542 | 0.2080 | 0.2144 | 0.2112 |
| 0.6304 | 8.0 | 360 | 1.2209 | 0.1920 | 0.1979 | 0.1950 |
| 0.5573 | 9.0 | 405 | 1.2925 | 0.1881 | 0.1814 | 0.1847 |
| 0.5048 | 10.0 | 450 | 1.3943 | 0.1731 | 0.1877 | 0.1804 |
| 0.4754 | 11.0 | 495 | 1.3058 | 0.1845 | 0.1817 | 0.1831 |