nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_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_logit_kd_stsb_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb_128", device_map="auto")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 |
|---|---|---|---|---|---|---|
| 2.5973 | 1.0 | 45 | 1.2342 | -0.0353 | -0.0325 | -0.0339 |
| 1.0952 | 2.0 | 90 | 1.1740 | 0.0434 | 0.0419 | 0.0426 |
| 1.0581 | 3.0 | 135 | 1.1533 | 0.0554 | 0.0563 | 0.0558 |
| 1.0455 | 4.0 | 180 | 1.2131 | 0.0656 | 0.0690 | 0.0673 |
| 0.9795 | 5.0 | 225 | 1.3883 | 0.0868 | 0.0858 | 0.0863 |
| 0.9197 | 6.0 | 270 | 1.4141 | 0.1181 | 0.1148 | 0.1165 |
| 0.8182 | 7.0 | 315 | 1.3460 | 0.1771 | 0.1853 | 0.1812 |
| 0.6796 | 8.0 | 360 | 1.1577 | 0.2286 | 0.2340 | 0.2313 |