nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola 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_data_aug_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_cola")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE COLA 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 | Matthews Correlation |
|---|---|---|---|---|
| 0.6247 | 1.0 | 1669 | 0.6837 | 0.1055 |
| 0.5458 | 2.0 | 3338 | 0.7216 | 0.1168 |
| 0.5041 | 3.0 | 5007 | 0.7127 | 0.1296 |
| 0.4445 | 4.0 | 6676 | 0.7718 | 0.1436 |
| 0.3961 | 5.0 | 8345 | 0.8417 | 0.1284 |
| 0.3603 | 6.0 | 10014 | 0.7805 | 0.1240 |