nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_sst2 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_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_sst2")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE SST2 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 | Accuracy |
|---|---|---|---|---|
| 1.0702 | 1.0 | 527 | 0.8404 | 0.7741 |
| 0.5148 | 2.0 | 1054 | 0.8241 | 0.7821 |
| 0.3924 | 3.0 | 1581 | 1.0815 | 0.7856 |
| 0.3413 | 4.0 | 2108 | 0.9159 | 0.7810 |
| 0.3068 | 5.0 | 2635 | 0.8836 | 0.7947 |
| 0.2837 | 6.0 | 3162 | 0.9832 | 0.7867 |
| 0.2648 | 7.0 | 3689 | 0.9633 | 0.7982 |