nyu-mll/glue
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How to use gokuls/mobilebert_sa_GLUE_Experiment_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_sst2_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_sst2_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_sa_GLUE_Experiment_sst2_256")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 |
|---|---|---|---|---|
| 0.4969 | 1.0 | 527 | 0.4333 | 0.8016 |
| 0.2781 | 2.0 | 1054 | 0.4999 | 0.7833 |
| 0.2274 | 3.0 | 1581 | 0.4782 | 0.7924 |
| 0.2 | 4.0 | 2108 | 0.5582 | 0.7936 |
| 0.1835 | 5.0 | 2635 | 0.4967 | 0.7913 |
| 0.1708 | 6.0 | 3162 | 0.5061 | 0.7856 |