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distilbert_sa_GLUE_Experiment_logit_kd_data_aug_stsb
This model is a fine-tuned version of distilbert-base-uncased on the GLUE STSB dataset. It achieves the following results on the evaluation set:
- Loss: 1.5182
- Pearson: 0.1994
- Spearmanr: 0.1993
- Combined Score: 0.1993
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
---|---|---|---|---|---|---|
0.3603 | 1.0 | 1259 | 1.5492 | 0.1887 | 0.2052 | 0.1969 |
0.0731 | 2.0 | 2518 | 1.5182 | 0.1994 | 0.1993 | 0.1993 |
0.0347 | 3.0 | 3777 | 1.5924 | 0.1665 | 0.1770 | 0.1717 |
0.0232 | 4.0 | 5036 | 1.5826 | 0.1596 | 0.1673 | 0.1635 |
0.0178 | 5.0 | 6295 | 1.5555 | 0.1812 | 0.1747 | 0.1779 |
0.0158 | 6.0 | 7554 | 1.5487 | 0.1878 | 0.1877 | 0.1878 |
0.0139 | 7.0 | 8813 | 1.6483 | 0.1789 | 0.1730 | 0.1759 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2