nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_logit_kd_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_logit_kd_stsb_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_stsb_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_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 |
|---|---|---|---|---|---|---|
| 2.1451 | 1.0 | 45 | 1.1476 | 0.0175 | 0.0051 | 0.0113 |
| 1.0864 | 2.0 | 90 | 1.2303 | 0.0364 | 0.0268 | 0.0316 |
| 1.0669 | 3.0 | 135 | 1.2794 | 0.0385 | 0.0299 | 0.0342 |
| 1.0484 | 4.0 | 180 | 1.2755 | 0.0394 | 0.0387 | 0.0391 |
| 1.0377 | 5.0 | 225 | 1.2931 | 0.0464 | 0.0436 | 0.0450 |
| 1.0279 | 6.0 | 270 | 1.2147 | 0.0491 | 0.0574 | 0.0532 |