distilbert_sa_GLUE_Experiment_data_aug_stsb_96
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: 2.7659
- Pearson: 0.1744
- Spearmanr: 0.1818
- Combined Score: 0.1781
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 |
|---|---|---|---|---|---|---|
| 2.2123 | 1.0 | 1259 | 2.7659 | 0.1744 | 0.1818 | 0.1781 |
| 0.689 | 2.0 | 2518 | 2.9511 | 0.1794 | 0.1858 | 0.1826 |
| 0.5239 | 3.0 | 3777 | 2.9043 | 0.1731 | 0.1733 | 0.1732 |
| 0.4171 | 4.0 | 5036 | 2.9002 | 0.1794 | 0.1788 | 0.1791 |
| 0.3402 | 5.0 | 6295 | 2.8190 | 0.1899 | 0.1926 | 0.1912 |
| 0.2843 | 6.0 | 7554 | 2.8391 | 0.1948 | 0.2004 | 0.1976 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2