nyu-mll/glue
Viewer • Updated • 1.49M • 463k • 495
How to use gokuls/mobilebert_add_GLUE_Experiment_sst2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/mobilebert_add_GLUE_Experiment_sst2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_sst2")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_sst2")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:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6635 | 1.0 | 527 | 0.6994 | 0.5390 |
| 0.5959 | 2.0 | 1054 | 0.6921 | 0.5665 |
| 0.5684 | 3.0 | 1581 | 0.7082 | 0.5516 |
| 0.5544 | 4.0 | 2108 | 0.6883 | 0.5619 |
| 0.5471 | 5.0 | 2635 | 0.6938 | 0.5940 |
| 0.5414 | 6.0 | 3162 | 0.7045 | 0.5803 |
| 0.5381 | 7.0 | 3689 | 0.7354 | 0.5654 |
| 0.5338 | 8.0 | 4216 | 0.7316 | 0.5826 |
| 0.3529 | 9.0 | 4743 | 0.4671 | 0.7970 |
| 0.2415 | 10.0 | 5270 | 0.4722 | 0.7982 |
| 0.2075 | 11.0 | 5797 | 0.4797 | 0.8062 |
| 0.1862 | 12.0 | 6324 | 0.5134 | 0.7993 |
| 0.1724 | 13.0 | 6851 | 0.5256 | 0.7993 |
| 0.1662 | 14.0 | 7378 | 0.5706 | 0.8028 |