nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_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_cola") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_cola")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_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 |
|---|---|---|---|---|
| 0.8105 | 1.0 | 67 | 0.6861 | 0.0 |
| 0.7967 | 2.0 | 134 | 0.6866 | 0.0 |
| 0.7956 | 3.0 | 201 | 0.6836 | 0.0 |
| 0.791 | 4.0 | 268 | 0.6788 | 0.0 |
| 0.7253 | 5.0 | 335 | 0.7158 | 0.0821 |
| 0.6322 | 6.0 | 402 | 0.6942 | 0.0650 |
| 0.5874 | 7.0 | 469 | 0.7295 | 0.0803 |
| 0.556 | 8.0 | 536 | 0.7735 | 0.0833 |
| 0.5308 | 9.0 | 603 | 0.7791 | 0.0970 |