nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_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_data_aug_stsb_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_stsb_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_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 |
|---|---|---|---|---|---|---|
| 1.0735 | 1.0 | 2518 | 2.8241 | 0.1664 | 0.1853 | 0.1759 |
| 0.6303 | 2.0 | 5036 | 2.7922 | 0.1559 | 0.1663 | 0.1611 |
| 0.501 | 3.0 | 7554 | 3.0939 | 0.1590 | 0.1630 | 0.1610 |
| 0.3527 | 4.0 | 10072 | 3.2284 | 0.1662 | 0.1715 | 0.1689 |
| 0.255 | 5.0 | 12590 | 2.9106 | 0.1775 | 0.1835 | 0.1805 |
| 0.194 | 6.0 | 15108 | 3.1703 | 0.1550 | 0.1626 | 0.1588 |
| 0.1563 | 7.0 | 17626 | 2.9109 | 0.1632 | 0.1691 | 0.1661 |