distilbert_sa_GLUE_Experiment_logit_kd_rte_256
This model is a fine-tuned version of distilbert-base-uncased on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 0.4233
- Accuracy: 0.4729
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 |
|---|---|---|---|---|
| 0.4233 | 1.0 | 10 | 0.4237 | 0.4729 |
| 0.4174 | 2.0 | 20 | 0.4245 | 0.4729 |
| 0.4184 | 3.0 | 30 | 0.4235 | 0.4729 |
| 0.4174 | 4.0 | 40 | 0.4250 | 0.4729 |
| 0.4174 | 5.0 | 50 | 0.4241 | 0.4729 |
| 0.4169 | 6.0 | 60 | 0.4238 | 0.4729 |
| 0.4164 | 7.0 | 70 | 0.4233 | 0.4729 |
| 0.4151 | 8.0 | 80 | 0.4233 | 0.4729 |
| 0.4109 | 9.0 | 90 | 0.4236 | 0.4729 |
| 0.3894 | 10.0 | 100 | 0.4484 | 0.4477 |
| 0.3551 | 11.0 | 110 | 0.4821 | 0.4585 |
| 0.3256 | 12.0 | 120 | 0.4913 | 0.4477 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2