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distilbert_sa_GLUE_Experiment_logit_kd_data_aug_sst2
This model is a fine-tuned version of distilbert-base-uncased on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4215
- Accuracy: 0.8635
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 | Accuracy |
|---|---|---|---|---|
| 0.434 | 1.0 | 4374 | 0.6108 | 0.8222 |
| 0.2568 | 2.0 | 8748 | 0.5465 | 0.8314 |
| 0.207 | 3.0 | 13122 | 0.5067 | 0.8463 |
| 0.1819 | 4.0 | 17496 | 0.4734 | 0.8544 |
| 0.1666 | 5.0 | 21870 | 0.4785 | 0.8578 |
| 0.1563 | 6.0 | 26244 | 0.4539 | 0.8589 |
| 0.1492 | 7.0 | 30618 | 0.4600 | 0.8589 |
| 0.1436 | 8.0 | 34992 | 0.4445 | 0.8647 |
| 0.1394 | 9.0 | 39366 | 0.4270 | 0.8727 |
| 0.1361 | 10.0 | 43740 | 0.4524 | 0.8601 |
| 0.1334 | 11.0 | 48114 | 0.4244 | 0.8693 |
| 0.1313 | 12.0 | 52488 | 0.4469 | 0.8635 |
| 0.1292 | 13.0 | 56862 | 0.4556 | 0.8498 |
| 0.1277 | 14.0 | 61236 | 0.4257 | 0.8635 |
| 0.1263 | 15.0 | 65610 | 0.4392 | 0.8567 |
| 0.1251 | 16.0 | 69984 | 0.4215 | 0.8635 |
| 0.124 | 17.0 | 74358 | 0.4289 | 0.8578 |
| 0.123 | 18.0 | 78732 | 0.4448 | 0.8601 |
| 0.1222 | 19.0 | 83106 | 0.4562 | 0.8555 |
| 0.1214 | 20.0 | 87480 | 0.4377 | 0.8544 |
| 0.1207 | 21.0 | 91854 | 0.4563 | 0.8555 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
