nyu-mll/glue
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How to use gokuls/mobilebert_add_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_add_GLUE_Experiment_stsb_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_stsb_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_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 |
|---|---|---|---|---|---|---|
| 5.0491 | 1.0 | 45 | 2.6735 | -0.0094 | -0.0099 | -0.0097 |
| 2.2021 | 2.0 | 90 | 3.1489 | 0.0389 | 0.0330 | 0.0359 |
| 2.1522 | 3.0 | 135 | 2.2943 | 0.0413 | 0.0270 | 0.0341 |
| 2.125 | 4.0 | 180 | 2.5078 | 0.0421 | 0.0274 | 0.0348 |
| 2.1328 | 5.0 | 225 | 2.2820 | 0.0445 | 0.0342 | 0.0393 |
| 2.0676 | 6.0 | 270 | 2.3672 | 0.0464 | 0.0393 | 0.0428 |
| 2.0545 | 7.0 | 315 | 2.6386 | 0.0506 | 0.0463 | 0.0485 |
| 2.0677 | 8.0 | 360 | 2.4397 | 0.0556 | 0.0574 | 0.0565 |
| 1.9988 | 9.0 | 405 | 2.4024 | 0.0601 | 0.0630 | 0.0615 |
| 1.9683 | 10.0 | 450 | 2.7224 | 0.0576 | 0.0646 | 0.0611 |