nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_rte 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_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_rte")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE RTE 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 |
|---|---|---|---|---|
| 0.2735 | 1.0 | 1136 | 0.5753 | 0.4657 |
| 0.2182 | 2.0 | 2272 | 0.5404 | 0.4621 |
| 0.2119 | 3.0 | 3408 | 0.5687 | 0.4765 |
| 0.2089 | 4.0 | 4544 | 0.5697 | 0.4838 |
| 0.2072 | 5.0 | 5680 | 0.5590 | 0.4801 |
| 0.2057 | 6.0 | 6816 | 0.5586 | 0.4838 |
| 0.2047 | 7.0 | 7952 | 0.5556 | 0.4801 |