nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_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_data_aug_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_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 |
|---|---|---|---|---|---|---|
| 1.0254 | 1.0 | 2518 | 2.8776 | 0.1575 | 0.1742 | 0.1659 |
| 0.5854 | 2.0 | 5036 | 3.1464 | 0.1591 | 0.1679 | 0.1635 |
| 0.4255 | 3.0 | 7554 | 2.8342 | 0.1765 | 0.1800 | 0.1782 |
| 0.2765 | 4.0 | 10072 | 2.8524 | 0.1815 | 0.1838 | 0.1827 |
| 0.1862 | 5.0 | 12590 | 2.9184 | 0.1736 | 0.1768 | 0.1752 |
| 0.1339 | 6.0 | 15108 | 2.9817 | 0.1688 | 0.1728 | 0.1708 |
| 0.1029 | 7.0 | 17626 | 2.9702 | 0.1618 | 0.1643 | 0.1631 |
| 0.0806 | 8.0 | 20144 | 3.0033 | 0.1588 | 0.1624 | 0.1606 |