nyu-mll/glue
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How to use gokuls/mobilebert_add_GLUE_Experiment_logit_kd_sst2_256 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_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_sst2_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_logit_kd_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:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5438 | 1.0 | 527 | 1.4012 | 0.5814 |
| 1.364 | 2.0 | 1054 | 1.5474 | 0.5413 |
| 1.2907 | 3.0 | 1581 | 1.5138 | 0.5642 |
| 1.257 | 4.0 | 2108 | 1.4409 | 0.5665 |
| 1.2417 | 5.0 | 2635 | 1.4473 | 0.5929 |
| 1.2056 | 6.0 | 3162 | 1.2641 | 0.7076 |
| 0.6274 | 7.0 | 3689 | nan | 0.4908 |
| 0.0 | 8.0 | 4216 | nan | 0.4908 |
| 0.0 | 9.0 | 4743 | nan | 0.4908 |
| 0.0 | 10.0 | 5270 | nan | 0.4908 |
| 0.0 | 11.0 | 5797 | nan | 0.4908 |