nyu-mll/glue
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How to use gokuls/mobilebert_sa_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_sa_GLUE_Experiment_sst2_128") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_sst2_128")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_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 |
|---|---|---|---|---|
| 0.5124 | 1.0 | 527 | 0.4330 | 0.8005 |
| 0.2842 | 2.0 | 1054 | 0.4711 | 0.8028 |
| 0.2267 | 3.0 | 1581 | 0.4593 | 0.7982 |
| 0.2025 | 4.0 | 2108 | 0.7141 | 0.7856 |
| 0.1849 | 5.0 | 2635 | 0.4771 | 0.7982 |
| 0.1754 | 6.0 | 3162 | 0.6028 | 0.7901 |