nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_stsb_128 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_sa_GLUE_Experiment_stsb_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_stsb_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_stsb_128")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 |
|---|---|---|---|---|---|---|
| 5.1866 | 1.0 | 45 | 2.5161 | -0.0355 | -0.0329 | -0.0342 |
| 2.1786 | 2.0 | 90 | 2.3527 | 0.0407 | 0.0395 | 0.0401 |
| 2.1136 | 3.0 | 135 | 2.3101 | 0.0543 | 0.0555 | 0.0549 |
| 2.1 | 4.0 | 180 | 2.4469 | 0.0624 | 0.0656 | 0.0640 |
| 1.9564 | 5.0 | 225 | 2.8646 | 0.0815 | 0.0817 | 0.0816 |
| 1.8611 | 6.0 | 270 | 2.5597 | 0.1089 | 0.1054 | 0.1071 |
| 1.6702 | 7.0 | 315 | 2.7087 | 0.1644 | 0.1666 | 0.1655 |
| 1.385 | 8.0 | 360 | 2.2718 | 0.2225 | 0.2230 | 0.2228 |
| 1.2518 | 9.0 | 405 | 2.4105 | 0.2134 | 0.2022 | 0.2078 |
| 1.143 | 10.0 | 450 | 2.5834 | 0.1998 | 0.2083 | 0.2040 |
| 1.0191 | 11.0 | 495 | 2.6132 | 0.1856 | 0.1896 | 0.1876 |
| 0.9431 | 12.0 | 540 | 2.8187 | 0.1784 | 0.1872 | 0.1828 |
| 0.8725 | 13.0 | 585 | 2.8360 | 0.1815 | 0.1879 | 0.1847 |