nyu-mll/glue
Viewer • Updated • 1.49M • 423k • 520
How to use masterkristall/bert_distillation_tiny with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="masterkristall/bert_distillation_tiny") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("masterkristall/bert_distillation_tiny")
model = AutoModelForSequenceClassification.from_pretrained("masterkristall/bert_distillation_tiny", 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:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4139 | 1.0 | 527 | 0.4204 | 0.8096 |
| 0.27 | 2.0 | 1054 | 0.4274 | 0.8257 |
| 0.2226 | 3.0 | 1581 | 0.4899 | 0.8245 |
| 0.1931 | 4.0 | 2108 | 0.4961 | 0.8222 |
| 0.1732 | 5.0 | 2635 | 0.5302 | 0.8222 |
| 0.1608 | 6.0 | 3162 | 0.5393 | 0.8234 |
| 0.152 | 7.0 | 3689 | 0.5562 | 0.8177 |
Base model
google/bert_uncased_L-2_H-128_A-2