<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
distilbert_add_GLUE_Experiment_logit_kd_sst2_256
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: 1.0990
- Accuracy: 0.7741
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 |
---|---|---|---|---|
1.6037 | 1.0 | 264 | 1.4539 | 0.5092 |
1.0469 | 2.0 | 528 | 1.4535 | 0.6961 |
0.6705 | 3.0 | 792 | 1.4747 | 0.7271 |
0.5309 | 4.0 | 1056 | 1.3223 | 0.75 |
0.4534 | 5.0 | 1320 | 1.1506 | 0.7718 |
0.4222 | 6.0 | 1584 | 1.1359 | 0.7661 |
0.3765 | 7.0 | 1848 | 1.0990 | 0.7741 |
0.3479 | 8.0 | 2112 | 1.1928 | 0.7672 |
0.323 | 9.0 | 2376 | 1.2111 | 0.7683 |
0.306 | 10.0 | 2640 | 1.2978 | 0.7672 |
0.2917 | 11.0 | 2904 | 1.2737 | 0.7649 |
0.2684 | 12.0 | 3168 | 1.1852 | 0.7729 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2