nyu-mll/glue
Viewer • Updated • 1.49M • 489k • 500
How to use gokuls/mobilebert_add_GLUE_Experiment_sst2_128 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_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_sst2_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_sst2_128")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 |
|---|---|---|---|---|
| 0.6677 | 1.0 | 527 | 0.6771 | 0.5757 |
| 0.5966 | 2.0 | 1054 | 0.7135 | 0.5424 |
| 0.5714 | 3.0 | 1581 | 0.7271 | 0.5550 |
| 0.5573 | 4.0 | 2108 | 0.6892 | 0.5619 |
| 0.501 | 5.0 | 2635 | 0.4546 | 0.7798 |
| 0.2856 | 6.0 | 3162 | 0.4613 | 0.8050 |
| 0.2288 | 7.0 | 3689 | 0.4543 | 0.7982 |
| 0.2027 | 8.0 | 4216 | 0.4662 | 0.7993 |
| 0.1883 | 9.0 | 4743 | 0.5168 | 0.8039 |
| 0.1779 | 10.0 | 5270 | 0.5748 | 0.7856 |
| 0.1691 | 11.0 | 5797 | 0.5196 | 0.8028 |
| 0.1596 | 12.0 | 6324 | 0.5943 | 0.7947 |