nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_data_aug_sst2_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_sst2_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_sst2_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_data_aug_sst2_256")This model is a fine-tuned version of google/mobilebert-uncased on the GLUE SST2 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 |
|---|---|---|---|---|
| 0.3529 | 1.0 | 8748 | 0.5172 | 0.7867 |
| 0.2729 | 2.0 | 17496 | 0.5752 | 0.7695 |
| 0.2317 | 3.0 | 26244 | 0.6663 | 0.7718 |
| 0.2039 | 4.0 | 34992 | 0.6987 | 0.7729 |
| 0.183 | 5.0 | 43740 | 0.9113 | 0.7810 |
| 0.1664 | 6.0 | 52488 | 0.8460 | 0.7844 |