nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_logit_kd_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_logit_kd_sst2_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_sst2_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_sst2_128", 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:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5487 | 1.0 | 527 | 1.3929 | 0.5780 |
| 1.3629 | 2.0 | 1054 | 1.4979 | 0.5505 |
| 1.1397 | 3.0 | 1581 | 1.3927 | 0.6755 |
| 0.5649 | 4.0 | 2108 | 0.7289 | 0.8073 |
| 0.4112 | 5.0 | 2635 | 0.7282 | 0.8073 |
| 0.3462 | 6.0 | 3162 | 0.7654 | 0.8050 |
| 0.3069 | 7.0 | 3689 | 0.8303 | 0.7970 |
| 0.2833 | 8.0 | 4216 | 0.8806 | 0.7924 |
| 0.2662 | 9.0 | 4743 | 0.9297 | 0.7959 |
| 0.2521 | 10.0 | 5270 | 1.0979 | 0.7718 |