nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_data_aug_mnli 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_mnli") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_mnli")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_mnli")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE MNLI 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 |
|---|---|---|---|---|
| 0.8429 | 1.0 | 62880 | 0.8755 | 0.6185 |
| 0.6713 | 2.0 | 125760 | 0.9512 | 0.6039 |
| 0.5387 | 3.0 | 188640 | 1.0796 | 0.5978 |
| 0.4297 | 4.0 | 251520 | 1.1877 | 0.5961 |
| 0.3405 | 5.0 | 314400 | 1.3154 | 0.5895 |
| 0.2693 | 6.0 | 377280 | 1.4320 | 0.5798 |