nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_rte_128 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_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_rte_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_rte_128", device_map="auto")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 |
|---|---|---|---|---|
| 0.28 | 1.0 | 1136 | 0.5452 | 0.4657 |
| 0.2191 | 2.0 | 2272 | 0.5774 | 0.4765 |
| 0.2124 | 3.0 | 3408 | 0.5632 | 0.5018 |
| 0.2095 | 4.0 | 4544 | 0.5727 | 0.4982 |
| 0.2076 | 5.0 | 5680 | 0.5487 | 0.4982 |
| 0.2063 | 6.0 | 6816 | 0.5625 | 0.4982 |