nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_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_rte") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_rte")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_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 |
|---|---|---|---|---|
| 0.4088 | 1.0 | 20 | 0.3931 | 0.5271 |
| 0.4081 | 2.0 | 40 | 0.3922 | 0.5271 |
| 0.4076 | 3.0 | 60 | 0.3910 | 0.5271 |
| 0.4068 | 4.0 | 80 | 0.3941 | 0.5343 |
| 0.4069 | 5.0 | 100 | 0.3924 | 0.5343 |
| 0.4022 | 6.0 | 120 | 0.3975 | 0.5343 |
| 0.3801 | 7.0 | 140 | 0.4060 | 0.5415 |
| 0.3447 | 8.0 | 160 | 0.5080 | 0.4982 |