distilbert_add_GLUE_Experiment_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: 2.2529
- Pearson: nan
- Spearmanr: nan
- Combined Score: nan
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 |
|---|---|---|---|---|---|---|
| 8.7243 | 1.0 | 23 | 6.6928 | nan | nan | nan |
| 7.9215 | 2.0 | 46 | 6.2710 | nan | nan | nan |
| 7.4296 | 3.0 | 69 | 5.8601 | nan | nan | nan |
| 6.9483 | 4.0 | 92 | 5.4460 | nan | nan | nan |
| 6.4768 | 5.0 | 115 | 5.0440 | nan | nan | nan |
| 5.9658 | 6.0 | 138 | 4.6523 | nan | nan | nan |
| 5.5067 | 7.0 | 161 | 4.2735 | nan | nan | nan |
| 5.0622 | 8.0 | 184 | 3.9107 | nan | nan | nan |
| 4.6133 | 9.0 | 207 | 3.5725 | nan | nan | nan |
| 4.2011 | 10.0 | 230 | 3.2630 | nan | nan | nan |
| 3.7839 | 11.0 | 253 | 2.9896 | nan | nan | nan |
| 3.4525 | 12.0 | 276 | 2.7549 | 0.0063 | 0.0066 | 0.0064 |
| 3.1246 | 13.0 | 299 | 2.5637 | -0.0161 | -0.0155 | -0.0158 |
| 2.8674 | 14.0 | 322 | 2.4155 | nan | nan | nan |
| 2.6317 | 15.0 | 345 | 2.3138 | nan | nan | nan |
| 2.4623 | 16.0 | 368 | 2.2596 | nan | nan | nan |
| 2.3397 | 17.0 | 391 | 2.2529 | nan | nan | nan |
| 2.2455 | 18.0 | 414 | 2.2910 | nan | nan | nan |
| 2.1984 | 19.0 | 437 | 2.3424 | nan | nan | nan |
| 2.1869 | 20.0 | 460 | 2.3424 | nan | nan | nan |
| 2.1982 | 21.0 | 483 | 2.3460 | nan | nan | nan |
| 2.195 | 22.0 | 506 | 2.3664 | -0.0023 | 0.0002 | -0.0011 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2