nyu-mll/glue
Viewer • Updated • 1.49M • 445k • 524
How to use gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_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_logit_kd_data_aug_sst2_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_sst2_256", device_map="auto")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 |
|---|---|---|---|---|
| 0.5038 | 1.0 | 8748 | 0.5359 | 0.8326 |
| 0.3322 | 2.0 | 17496 | 0.5152 | 0.8394 |
| 0.2798 | 3.0 | 26244 | 0.5338 | 0.8417 |
| 0.2516 | 4.0 | 34992 | 0.4846 | 0.8555 |
| 0.2332 | 5.0 | 43740 | 0.4754 | 0.8635 |
| 0.2202 | 6.0 | 52488 | 0.5100 | 0.8589 |
| 0.2096 | 7.0 | 61236 | 0.5521 | 0.8486 |
| 0.2009 | 8.0 | 69984 | 0.5036 | 0.8589 |
| 0.1936 | 9.0 | 78732 | 0.5075 | 0.8521 |
| 0.1876 | 10.0 | 87480 | 0.5093 | 0.8544 |