nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_stsb_256 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_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_stsb_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_stsb_256")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 |
|---|---|---|---|---|---|---|
| 0.535 | 1.0 | 2518 | 1.3502 | 0.1595 | 0.1783 | 0.1689 |
| 0.3149 | 2.0 | 5036 | 1.4771 | 0.1504 | 0.1610 | 0.1557 |
| 0.2467 | 3.0 | 7554 | 1.5606 | 0.1609 | 0.1660 | 0.1634 |
| 0.1704 | 4.0 | 10072 | 1.5334 | 0.1662 | 0.1766 | 0.1714 |
| 0.1257 | 5.0 | 12590 | 1.4389 | 0.1712 | 0.1783 | 0.1747 |
| 0.0976 | 6.0 | 15108 | 1.5827 | 0.1437 | 0.1526 | 0.1482 |