nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli 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_wnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE WNLI 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 |
|---|---|---|---|---|
| 0.6415 | 1.0 | 435 | 2.5287 | 0.1268 |
| 0.4894 | 2.0 | 870 | 3.5123 | 0.1268 |
| 0.4427 | 3.0 | 1305 | 4.8804 | 0.0986 |
| 0.4026 | 4.0 | 1740 | 7.2410 | 0.0986 |
| 0.3707 | 5.0 | 2175 | 10.5770 | 0.0845 |
| 0.3376 | 6.0 | 2610 | 7.2101 | 0.0986 |