nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_stsb_256 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_add_GLUE_Experiment_stsb_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_stsb_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_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 |
|---|---|---|---|---|---|---|
| 4.3289 | 1.0 | 45 | 2.5185 | 0.0152 | 0.0186 | 0.0169 |
| 2.1532 | 2.0 | 90 | 2.5569 | 0.0388 | 0.0235 | 0.0312 |
| 2.1161 | 3.0 | 135 | 2.2965 | 0.0399 | 0.0217 | 0.0308 |
| 2.1083 | 4.0 | 180 | 2.4191 | 0.0409 | 0.0284 | 0.0347 |
| 2.0885 | 5.0 | 225 | 2.5610 | 0.0458 | 0.0382 | 0.0420 |
| 2.0602 | 6.0 | 270 | 2.3788 | 0.0473 | 0.0444 | 0.0458 |
| 2.0283 | 7.0 | 315 | 2.4459 | 0.0571 | 0.0527 | 0.0549 |
| 1.9677 | 8.0 | 360 | 2.4995 | 0.0614 | 0.0604 | 0.0609 |