distilbert_sa_GLUE_Experiment_logit_kd_data_aug_rte_192
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: 0.5485
- Accuracy: 0.5199
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 |
|---|---|---|---|---|
| 0.348 | 1.0 | 568 | 0.5499 | 0.4874 |
| 0.2888 | 2.0 | 1136 | 0.5640 | 0.4982 |
| 0.2849 | 3.0 | 1704 | 0.5618 | 0.5199 |
| 0.2833 | 4.0 | 2272 | 0.5618 | 0.5018 |
| 0.2823 | 5.0 | 2840 | 0.5610 | 0.5090 |
| 0.2816 | 6.0 | 3408 | 0.5485 | 0.5199 |
| 0.281 | 7.0 | 3976 | 0.5527 | 0.5126 |
| 0.2805 | 8.0 | 4544 | 0.5578 | 0.5054 |
| 0.2798 | 9.0 | 5112 | 0.5575 | 0.5343 |
| 0.2796 | 10.0 | 5680 | 0.5533 | 0.5199 |
| 0.2793 | 11.0 | 6248 | 0.5534 | 0.5090 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2