nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_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_data_aug_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_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 |
|---|---|---|---|---|---|---|
| 0.5057 | 1.0 | 2518 | 1.4410 | 0.1664 | 0.1770 | 0.1717 |
| 0.2904 | 2.0 | 5036 | 1.5531 | 0.1681 | 0.1758 | 0.1720 |
| 0.2164 | 3.0 | 7554 | 1.5013 | 0.1732 | 0.1766 | 0.1749 |
| 0.1385 | 4.0 | 10072 | 1.4793 | 0.1854 | 0.1821 | 0.1837 |
| 0.0944 | 5.0 | 12590 | 1.5300 | 0.1694 | 0.1741 | 0.1717 |
| 0.0682 | 6.0 | 15108 | 1.5759 | 0.1695 | 0.1691 | 0.1693 |