nyu-mll/glue
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How to use gokuls/tiny-bert-sst2-1_mobilebert-only-distillation with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="gokuls/tiny-bert-sst2-1_mobilebert-only-distillation") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/tiny-bert-sst2-1_mobilebert-only-distillation")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/tiny-bert-sst2-1_mobilebert-only-distillation")This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue 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 |
|---|---|---|---|---|
| 0.4252 | 1.0 | 4210 | 2.6253 | 0.8142 |
| 0.519 | 2.0 | 8420 | 2.4860 | 0.8245 |
| 0.4986 | 3.0 | 12630 | 2.2808 | 0.8291 |
| 0.4454 | 4.0 | 16840 | 2.5185 | 0.8280 |
| 0.3912 | 5.0 | 21050 | 2.3982 | 0.8257 |
| 0.3561 | 6.0 | 25260 | 2.4030 | 0.8211 |