nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_logit_kd_stsb with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_add_GLUE_Experiment_logit_kd_stsb") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_stsb")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_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 |
|---|---|---|---|---|---|---|
| 1.7607 | 1.0 | 45 | 1.2881 | 0.0340 | 0.0258 | 0.0299 |
| 1.0763 | 2.0 | 90 | 1.1761 | 0.0478 | 0.0438 | 0.0458 |
| 1.0466 | 3.0 | 135 | 1.1550 | 0.0509 | 0.0390 | 0.0450 |
| 1.0685 | 4.0 | 180 | 1.1407 | 0.0533 | 0.0481 | 0.0507 |
| 1.0449 | 5.0 | 225 | 1.1527 | 0.0562 | 0.0478 | 0.0520 |
| 1.0303 | 6.0 | 270 | 1.2257 | 0.0580 | 0.0606 | 0.0593 |
| 1.0006 | 7.0 | 315 | 1.2018 | 0.0711 | 0.0736 | 0.0724 |
| 0.9661 | 8.0 | 360 | 1.2391 | 0.0716 | 0.0848 | 0.0782 |
| 0.9524 | 9.0 | 405 | 1.2005 | 0.0795 | 0.0749 | 0.0772 |