nyu-mll/glue
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How to use gokuls/mobilebert_sa_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_sa_GLUE_Experiment_logit_kd_stsb_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_stsb_256", 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 |
|---|---|---|---|---|---|---|
| 2.075 | 1.0 | 45 | 1.1337 | 0.0151 | 0.0166 | 0.0159 |
| 1.0752 | 2.0 | 90 | 1.1691 | 0.0603 | 0.0648 | 0.0626 |
| 1.0435 | 3.0 | 135 | 1.2035 | 0.0659 | 0.0746 | 0.0703 |
| 1.0472 | 4.0 | 180 | 1.1488 | 0.0764 | 0.0817 | 0.0790 |
| 0.9687 | 5.0 | 225 | 1.5234 | 0.0979 | 0.0959 | 0.0969 |
| 0.9016 | 6.0 | 270 | 1.2243 | 0.1434 | 0.1381 | 0.1408 |