nyu-mll/glue
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How to use gokuls/mobilebert_add_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_add_GLUE_Experiment_sst2_256") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/mobilebert_add_GLUE_Experiment_sst2_256")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/mobilebert_add_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 |
|---|---|---|---|---|
| 0.6662 | 1.0 | 527 | 0.6814 | 0.5562 |
| 0.5954 | 2.0 | 1054 | 0.7090 | 0.5493 |
| 0.5689 | 3.0 | 1581 | 0.7150 | 0.5596 |
| 0.5546 | 4.0 | 2108 | 0.6893 | 0.5539 |
| 0.5473 | 5.0 | 2635 | 0.7051 | 0.5872 |
| 0.5421 | 6.0 | 3162 | 0.6983 | 0.5872 |