nyu-mll/glue
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How to use gokuls/tiny-bert-sst2-1_mobilebert_and_bert-multi-teacher-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_and_bert-multi-teacher-distillation") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gokuls/tiny-bert-sst2-1_mobilebert_and_bert-multi-teacher-distillation")
model = AutoModelForSequenceClassification.from_pretrained("gokuls/tiny-bert-sst2-1_mobilebert_and_bert-multi-teacher-distillation", device_map="auto")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 |
|---|---|---|---|---|
| 0.6309 | 1.0 | 4210 | 2.5545 | 0.8280 |
| 0.6148 | 2.0 | 8420 | 2.4299 | 0.8245 |
| 0.595 | 3.0 | 12630 | 2.3525 | 0.8211 |
| 0.5388 | 4.0 | 16840 | 2.5075 | 0.8234 |