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distilbert_sa_GLUE_Experiment_logit_kd_pretrain_wnli
This model is a fine-tuned version of gokuls/distilbert_sa_pre-training-complete on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.3499
- Accuracy: 0.5493
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 |
|---|---|---|---|---|
| 0.3552 | 1.0 | 3 | 0.3512 | 0.4085 |
| 0.3495 | 2.0 | 6 | 0.3540 | 0.2817 |
| 0.3471 | 3.0 | 9 | 0.3499 | 0.5493 |
| 0.3473 | 4.0 | 12 | 0.3514 | 0.5634 |
| 0.3476 | 5.0 | 15 | 0.3536 | 0.5070 |
| 0.3465 | 6.0 | 18 | 0.3576 | 0.1831 |
| 0.3463 | 7.0 | 21 | 0.3589 | 0.2113 |
| 0.3449 | 8.0 | 24 | 0.3598 | 0.2958 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
