nyu-mll/glue
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How to use masterkristall/bert_distillation_0 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="masterkristall/bert_distillation_0") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("masterkristall/bert_distillation_0")
model = AutoModelForSequenceClassification.from_pretrained("masterkristall/bert_distillation_0", 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 |
|---|---|---|---|---|
| 1.2697 | 1.0 | 527 | 1.0445 | 0.8200 |
| 0.6784 | 2.0 | 1054 | 1.0168 | 0.8177 |
| 0.5206 | 3.0 | 1581 | 1.1356 | 0.8108 |
| 0.4383 | 4.0 | 2108 | 1.1437 | 0.8280 |
| 0.3844 | 5.0 | 2635 | 1.1687 | 0.8268 |
| 0.3547 | 6.0 | 3162 | 1.1784 | 0.8303 |
| 0.3373 | 7.0 | 3689 | 1.2045 | 0.8280 |
Base model
google/bert_uncased_L-2_H-128_A-2