nyu-mll/glue
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How to use marlex7/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="marlex7/tiny-bert-sst2-distilled") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("marlex7/tiny-bert-sst2-distilled")
model = AutoModelForSequenceClassification.from_pretrained("marlex7/tiny-bert-sst2-distilled", 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 |
|---|---|---|---|---|
| 2.4226 | 1.0 | 527 | 1.7918 | 0.7844 |
| 1.5656 | 2.0 | 1054 | 1.6692 | 0.7947 |
Base model
google/bert_uncased_L-2_H-128_A-2