nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_data_aug_rte_256 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_sa_GLUE_Experiment_data_aug_rte_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_rte_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_rte_256")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 |
|---|---|---|---|---|
| 0.2703 | 1.0 | 1136 | 3.2768 | 0.4657 |
| 0.0555 | 2.0 | 2272 | 3.0847 | 0.4874 |
| 0.0253 | 3.0 | 3408 | 5.4968 | 0.5018 |
| 0.0149 | 4.0 | 4544 | 5.6020 | 0.4982 |
| 0.0104 | 5.0 | 5680 | 6.6683 | 0.5090 |
| 0.0082 | 6.0 | 6816 | 8.2220 | 0.5090 |
| 0.0062 | 7.0 | 7952 | 8.2179 | 0.5054 |